Radiotherapy is one of the main treatment modalities available to treat cancer. Radiotherapy treatment plans are created based on CT scans of the patient. In such scans the macroscopic tumor is visible, but microscopic disease present in the surrounding tissue cannot be observed. To achieve an optimal clinical outcome, both the macroscopic and the microscopic disease must be treated. Currently, the macroscopic tumor is extended by a margin into the Clinical Target Volume (CTV) to include the microscopic disease in the treated volume. The same margin is used for all patients, although the extent of microscopic disease is patient-specific and can vary largely among patients. In this study, probabilistic treatment planning was investigated as a method to replace the margin concept. Probabilistic models were created by explicitly modeling uncertainties in the microscopic disease into an objective function used in the treatment plan optimization. By optimizing either the expected Tumor Control Probability (ETCP) or the expected Logarithmic Tumor Control Probability (ELTCP), optimal dose distributions could be obtained. Two different one-dimensional models for probabilistic treatment planning were investigated. In the first model, the uncertainty in the extent of the microscopic disease was modeled into an objective function. This was done using a function that describes the probability of finding microscopic disease at a certain distance from the macroscopic disease. In the second model, the uncertainty in the tumor cell density in the microscopic disease area was modeled into an objective function. The uncertainty was modeled by defining the tumor cell density field as a random field and generating different realizations of the tumor cell density field using a Karhunen-Loève (KL) expansion. For the first model, both the ETCP and the ELTCP were used as objective functions and in the second model, only the ETCP was used as an objective function. Furthermore, a penalized ETCP objective function was investigated for both models. In this penalized objective function a penalty on the dose was used to allow for controlling the balance between tumor control and sparing of normal tissue. Using the first model, two different types of dose distributions were found. When the ETCP was optimized, the maximum dose was given to as large a volume as possible and no dose was given in the rest of the investigated volume. When the ELTCP was optimized, dose was given throughout the volume, so that the whole volume received as much dose as possible. Optimization of both objectives resulted in good tumor control. When the penalized ETCP was optimized, dose was given to a much smaller part of the volume than with the unpenalized objective, while the tumor control was still good. Using the second model, it was shown that the KL-expansion is a promising method to model the uncertainty in tumor cell density. Different shapes of the input mean tumor cell density field were investigated. Optimizing the ETCP resulted in realistic dose distributions. Good tumor control was obtained for the different shapes of the input mean tumor cell density field. Furthermore, using the penalized ETCP, good tumor control was retained, while the dose deposited in the volume was decreased. In conclusion, probabilistic treatment planning promises to be a good alternative to the current margin concept. It was shown that good tumor control could be achieved in the microscopic disease area using probabilistic objective functions. Both models showed promising results and the penalized objectives showed that it is possible to balance between tumor control in the microscopic disease area and sparing of normal tissue. Additional research is necessary to extend the one-dimensional KL-model into a more detailed three-dimensional model. Furthermore, the objectives need to be implemented in treatment planning systems to create real patient plans. Such studies should be performed in cooperation with clinicians and radiologists.