Aiming at the problem of low coverage of wireless sensor networks deployed randomly, a Multi Strategy gray wolf (MSGWO) algorithm for wireless sensor network coverage optimization is proposed. Firstly, in order to balance the global and local search, a nonlinear convergence factor of hyperbolic tangent is proposed; Secondly, the bounding step size is reconstructed by differential mutation to reduce the probability of the algorithm falling into local optimization; Then, in order to speed up the convergence speed and accuracy of the algorithm, the gray wolf position is updated by using the transient search optimization equation; Then, Levy flight strategy is integrated to increase the diversity of space search; Finally, the boundary offside strategy is introduced to avoid the relocation of gray wolf individuals. The simulation results show that compared with SSA, LGWO, PSO and PSOGWO, the average coverage increment of MSGWO algorithm is 12.52%, 6.054%, 7.53% and 3.45% respectively. This algorithm has higher average coverage and better node distribution.