Abstract A new method is described for complex process control with the coordinating control unit based upon a genetic algorithm. The algorithm for the control of complex processes controlled by PID and fuzzy regulators at the first level and a coordinating unit at the second level has been theoretically laid out. A genetic algorithm and its application to the proposed control method have been described in detail. The idea has been verified experimentally and by simulation in a twostage laboratory plant. Minimal energy consumption criteria limited by given process response constraints have been applied, and improvements in relation to other known optimizing methods have been made. Independent and noncoordinating PID and fuzzy regulator parameter tuning has been performed using a genetic algorithm and the results achieved are the same or better than using traditional optimizing methods and at the same time the method proposed can be easily automated. Multilevel coordinated control using a genetic algorithm applied to a PID and a fuzzy regulator has been researched. The results of various traditional optimizing methods have been compared with an independent noncoordinating control and multilevel coordinating control using a genetic algorithm. The best results have been achieved with the multilevel coordinating fuzzy control optimized by a genetic algorithm.