Aiming at the phenomenon of low coverage and uneven nodes in three-dimensional wireless sensor networks with random deployment, taking the coverage as the fitness function, a coverage optimization algorithm for three-dimensional wireless sensor networks based on EGWOEO algorithm is proposed. Firstly, tent chaotic map is used to initialize the population to increase the diversity of the population. Secondly, reverse learning strategy is used to increase the global search ability. Thirdly, the hyperbolic tangent Gaussian strategy is integrated to strengthen the optimization ability of the algorithm. Then, a nonlinear convergence factor of sine and cosine function is proposed to balance the global and local search. Finally, the population position update equation is improved to speed up the convergence speed and accuracy of the algorithm. The improved EGWOEO algorithm is applied to 3D WSN coverage optimization, the simulation results show that compared with GWO, PSOGWO and LGWO algorithms, the average increment of 3D WSN coverage of EGWOEO algorithm is 11.023%, 10.662% and 12.401% respectively, which improves the uneven distribution of nodes and improves the utilization of nodes.