This paper considers a genetic algorithm modification based on the annealing simulation and novelty search in applying to the scheduling problem. We propose a multiagent genetic optimization method implementing different decision searching strategies, including a simulation module. The comparison of the different scheduling methods has shown: firstly, the unsuitability of the MS Project planning method to solve the formulated problem; and secondly, both the advantage of the multiagent genetic optimization method in terms of economic effect and disadvantage in terms of performance. Some techniques to reduce the impact of the method’s disadvantage are proposed in the conclusion, as well as the aims of future work.