In order to solve the problem of slow convergence rate and easy to get into local optimum when solving multi-objective flexible job shop scheduling problem, with traditional intelligence optimization algorithm, this paper proposes a hybrid optimization algorithm that fuses three important performance parameters of QPSO with the wolf pack algorithm. Firstly, a multi-objective mathematical model with maximum completion time, total machine load and bottleneck machine load as optimization indexes is constructed. Secondly, the probability density function of Gaussian distribution is used to generate random variables for population initialization so as to improve the diversity and quality of the initial population. Neighborhood structure search strategy is used to adjust the optimal sequence, and the global search performance of the algorithm is improved. Finally, the matter-element analysis method is used to update the population and to improve the adaptive ability of the population. By comparing with the simulation experiment of many intelligent optimization algorithms, we can see that the hybrid wolf pack algorithm proposed in this paper is feasible and advantageous for solving the multi-objective flexible job shop scheduling problem.