Scheduling problems are present in many real-world situations, such as construction projects, manufacturing processes, or train timetabling. One common formalization is the Resource Constrained Project Scheduling Problem (RCPSP), where the goal is to find an optimal schedule given limited resources. Traditional algorithms optimize for project duration under deterministic assumptions, which could lead to poor performance under uncertainty. This thesis explores how to create robust schedules, meaning they can withstand uncertainty, for stochastic task durations. Robust schedules minimize delays, measured as the difference between a task's completion time and its deadline. The method proposed uses probabilistic programming as a tool to accomplish this. A robustness distribution over schedules is inferred using importance sampling, and a robust schedule can be selected from that explored distribution. Importantly, this approach presented in this thesis can be applied on top of existing scheduling and simulation algorithms without requiring any knowledge or changes to themselves. Created schedules can also be abstracted, thus do not need to be analyzed or seen. This makes the proposed method general and easy to adopt in practice.