Computational offloading is an essential technology in mobile edge computing, and for the shortage of computational offloading strategies in multi-user multi-MEC server scenarios, this paper proposes a hybrid artificial bee colony algorithm (artificial reverse sine-cosine, ARSC). the ARSC algorithm first initializes the population using a Opposition-Based Learning strategy to optimize the initial solution of the population; then in the employment bee stage by using the sine-cosine algorithm of global optimal bootstrap information to improve the global search capability of the algorithm, and finally, to balance the global search capability and local search capability of the algorithm, the step size factor of the algorithm is improved by combining convex and concave functions. The offloading strategies based on particle swarm algorithm and artificial bee colony algorithm are contrasted with the ARSC approach through simulated studies. The experimental findings demonstrate that the convergence, system latency, and system energy consumption have all improved with the ARSC method.