The Software Project Scheduling (SPS) problem refers to the distribution of tasks during a software project lifetime. Software development involves managing human resources and a total budget in an optimal way for a successful project which, in turn, demonstrates the importance of the SPS problem for software companies. This paper proposes a novel formulation for the SPS problem which takes into account actual issues such as the productivity of the employees at performing different tasks. The formulation also provides project managers with robust solutions arising from an analysis of the inaccuracies in task-cost estimations. An experimental study is presented which compares the resulting project plans and analyses the performance of four different well-know evolutionary algorithms over two sets of realistic instances representing the problem. Statistical parameters are also provided in order to help the project manager in the decision process.