Resource constrained project scheduling problem is a problem in which it is necessary to find a schedule of activities that will meet the resource and precedence constraints and at the same time optimize one or more criteria. This problem belongs to the class of NP-hard problems, which is why it is not possible to find the optimal solution for most problem instances. Accordingly, numerous heuristic methods are being developed whose usage is mostly limited to static environments, while priority rules are one of the few methods that can be applied in dynamic environments as well. The development of priority rules is a demanding job, which is why procedures for the automated development of scheduling rules are increasingly being used. This thesis is focused on the design of priority rules for resource constrained project scheduling problem using genetic programming and is divided into three main parts. The first part is focused on the development of priority rules, in the second part ensembles od priority rules were used to further increase the quality of results achieved by the developed priority rules, while the third part brings the adaptation of developed rules for use in static environments. The conducted experiments show that the developed rules achieve significantly better results than those achieved by the existing priority rules and that their additional adaptation, depending on the environment in which they are used, can lead to significant improvements in results.