Investment projects in railway infrastructure networks are needed to maintain a high condition of the network, maximize the capacity of the network, and to keep the risk low. Railway organizations are aiming to achieve these goals, but also want to minimize the costs of the investments. The desires of stakeholders in railway networks such as the government, passengers, and freight users are often not aligned. Decision makers in this field need to make trade-offs between the conflicting objectives. In the decision-making process regarding investment projects, it is important that the multiple objectives are considered explicitly. A multi-objective optimization problem formulation is proposed for the investment project portfolio optimization problem. Solving a multi-objective optimization problem means to identify the set of Pareto optimal solutions. These Pareto optimal solutions are used by decision makers to gain insight in the effects on the objectives, and to make well-founded trade-offs. An approach is proposed to include the uncertain nature of investment projects in the problem formulation, without increasing the complexity of the problem too much. This is done by introducing lower and upper bounds for uncertain variables. By optimizing not only the expected values of the objective functions, but also the lower and upper bound, one is able to identify the solutions that give satisfactory results for all the scenarios. Project portfolios that are Pareto optimal for the lower bound, expected value, and upper bound, are considered as robust choices with respect to the uncertainties in the objective values. Railway infrastructures typically consist of many assets with a long lifespan. The prediction horizon and the level of detail considered in the optimization problem need to be chosen carefully to avoid extremely large computation times. Even for a modest level of detail, the problem size is too large to check all the possible solutions within acceptable time. The computation time is also dependent on the algorithm that is used to search for the Pareto optimal set. Next to the computation time, several performance indicators are selected to measure the quality of the approximation of the Pareto optimal set that is provided by an algorithm. Two algorithms with different approaches are proposed to approximate the Pareto optimal set, the repeated ε-constraint algorithm and the widely used genetic algorithm NSGA II. Some benchmark problems are introduced to gain insight into the working of the proposed algorithms. A case study is performed for an artificial data set for investment projects in a railway network. The selected performance indicators are used to analyze the results of the case studies for the proposed algorithms with different settings. The repeated ε-constraint algorithm is able to find a close representation of the Pareto optimal set, and has a very stable performance due to the established search method of the CPLEX solver. However, obtaining these results requires an excessive amount of computation time. The genetic algorithm NSGA II is much faster, and for carefully tuned settings, the quality of the approximation set is fine. The coverage of the NSGA II is considered sufficient, so due to the reduced computation time, the NSGA II is recommended to use in the decision support process of selecting investment project portfolios in railway infrastructure networks.