In view of the wolf pack algorithm has good solving ability in single objective optimization problems, a multi-objective wolf pack algorithm(MOWPA-AG) based on adaptive grouping and updating of crowded distance is proposed by taking the advantages of the wolf pack biological habit and being used to solve multi-objective optimization problems. Firstly, an adaptive grouping strategy considering population diversity and dispersed search is proposed to simulate family aggregation in wolf packs. The strategy stratifies populations, separates populations and helps population diffusion search Pareto optimal solutions. Then, a population renewal mechanism based on crowding distance is designed, which enables the population to maintain rapid evolution while obtaining the optimal solution set. In order to verify the performance of the proposed algorithm, nine different benchmark testing problems are tested, and the effectiveness of the proposed algorithm is verified by comparing with other classic and recent multi-objective optimization algorithms. Finally, the MOWPA-AG is applied to solve the problem of four-bar truss structure in practical engineering, which shows the universality of the proposed algorithm.