Abstract: For the multi-objective flexible job shop scheduling problem(MOFJSP), an improved grey wolf algorithm (IGWO) was proposed to solve the multi-objective optimization considering the completion time, total energy consumption and total machine load. IGWO uses binary coding and population initialization based on weights, adds genetic operators to update the coding iteratively, uses Pareto non-dominated sorting and crowding degree distance to find the non-dominated solution in the iterative process, and saves the non-dominated solution set in the external archive. The nonlinear convergence factor is introduced to balance the global search ability and local search ability of the algorithm. By introducing an improved catfish effect strategy, the population vitality is guaranteed, the convergence accuracy of the algorithm is improved, and the algorithm is avoided to fall into the local optimal solution. Finally, the feasibility and superiority of the algorithm are verified by a machining shop example and a comparative experiment.