In order to improve the problem of poor convergence performance and easy to fall into local optimum of SSA, the golden sine salp swarm algorithm (MGSSA) with multi-strategy is proposed. First, the selection opposition strategy is used to improve the population quality by calculating selection opposite solutions for individuals in the population that completely deviate from the optimal individual search direction. After that, 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, while facilitating 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 the novel improved salp swarm algorithms, and its performance is applied to test the solution of engineering optimization problems in tension-pressure spring design problems. The results show that MGSSA has high convergence accuracy and stability, and performs well in solving engineering problems.