To improve the poor convergence performance and escape from local optimum of Salp Swarm Algorithm (SSA), a Golden sine SSA with Multi-strategy (MGSSA) is proposed.First, the Selective Opposition-Based Learning (SOBL) strategy is used to improve the population quality by calculating selective opposite solutions for individuals in the population that completely deviate from the optimal individual search direction.Then the optimal individual and elite mean individual are added in the follower position update phase to speed up the convergence of the algorithm.Finally, the golden sine algorithm variation strategy is selected based on the probability to further improve the quality of the solution, and facilitate the algorithm to jump out of the local optimum later.In this study, experiments are conducted on 14 benchmark test functions to compare with other swarm intelligence optimization algorithms and novel improved SSA, and then the proposed approach is applied to test the solution of engineering optimization problems in tension/compression spring design.The results show that the proposed MGSSA has high convergence accuracy and stability, and performs well in solving engineering problems.