Simultaneous planning of project scheduling and material procurement can lead to the project execution costs improvement. Hence, the issue has been addressed in this paper by a robust mixed-integer programming mathematical model, which aims to minimize the corresponding costs and maximize the schedule robustness. The given approach is able to control the degree of solution conservatism, in regard to probabilistic bounds on constraint violation. The proposed model takes the uncertainty issue into account from both viewpoints of activities duration time and execution costs. The NSGA-II and a modified version of multi-objective differential evolution algorithm have been applied as the solution methodologies. Moreover, the principal factors are calibrated by the Taguchi method to provide robustness to the obtained results. Finally, the performance of the solution methods is compared according to a varied set of instances to test their applicability and efficiency.