Abstract: A hybrid genetic algorithm based on Pareto was proposed and applied to the multi-objective flexible job shop scheduling problem (FJSP), and a multi-objective FJSP optimization model was developed including make-span, total tardiness and machine utilization rate. The algorithm combines Pareto ranking strategy with Pareto competition method. The operation-based encoding and an active scheduling decoding method are employed. In order to promote solution diversity, the niche technology and many kinds of crossover operations are used here. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. The set of Pareto optimum solutions is obtained. In the end, a simulation experiment was carried out to illustrate that the proposed method can solve multi-objective FJSP effectively.