As part of the effort to reduce the cost of offshore wind energy, this MSc Thesis deals with the layout optimization of an Offshore Wind Farm affected by wake effects, cable topology and support structure variation. The main objective of the current MSc Thesis is to investigate how important each of these aspects for the layout optimization is. The final outcome of the project is an optimization tool that tries to find the optimal Offshore Wind Farm (OWF) layout in terms of the lowest Levelized Production Cost (LPC). This optimization tool depends on the analyzer algorithm, which calculates the objective function of the optimizer. The analyzer consists of three different elements that are combined together so that the Levelized Production Cost will be calculated. Thus, the objective function will be the LPC. The analyzer elements are the wake effects, cable topology and support structure variation. For the support structure variation, part of the MZ Tool developed by professor Dr. Michiel Zaaijer is used. Using this tool, the support structure dimensions and costs can be determined. Regarding the cable topology, a hybrid approach between Planar Open Savings(POS)and Esau-Williams(EW)heuristics is used so that the performance of EW can be improved for multiple cables and lower infield cable cost will be achieved. Finally, the Jensen wake model is used in an algorithm so that the wake effects can be determined. The output of that algorithm is the annual energy yield and the LPC. The optimization tool is based on the Genetic Algorithm(GA)logic. The performance of the optimization tool is evaluated both cost and time-wise by implementing four scenarios. In these scenarios some parameters of the GA are changed so that the behavior of the optimization tool can be examined. Finally, different case studies, related to the seabed shape and to the three ingredients in the analyzer, are examined. These case studies will show how the changes in the analyzer can affect the optimality of an OWF. More specifically, it is found that all three elements in the analyzer affect the layout optimization. In addition, it is concluded that the support structure variation has the largest contribution to the layout optimization compared to the cable topology. Regarding the computation time, it is higher in the case that there is support structure variation, since it takes more time for the analyzer to calculate all the costs.