Aiming at the problems of low coverage rate and uneven distribution of nodes in Wireless Sensor Network （WSN）， a node coverage optimization strategy based on Multi-strategy Improved Butterfly Optimization Algorithm （MIBOA） was proposed. Firstly， the basic Butterfly Optimization Algorithm （BOA） was combined with Sparrow Search Algorithm （SSA） to improve the search process. Secondly， the adaptive weight coefficient was introduced to improve the optimization accuracy and convergence speed. Finally， the current best individual was perturbed by Cauchy mutation to improve the robustness of the algorithm. The optimization experiment results on benchmark functions show that， MIBOA can basically solve the optimal value of the test function within 3 seconds， and the average accuracy of convergence is improved by 97.96% compared with BOA. MIBOA was applied to the WSN node coverage optimization problem. Compared with optimization results of BOA and SSA， the node coverage rate was improved by 3.63 percentage points at least. Compared with the Improved Grey Wolf Optimization algorithm （IGWO）， the deployment time was shortened by 145.82 seconds. Compared with the Improved Whale Optimization Algorithm （IWOA）， the node coverage rate was increased by 0.20 percentage points and the time was shortened by 1 112.61 seconds. In conclusion， MIBOA can improve the node coverage rate and reduce the redundant coverage rate， and effectively prolong the lifetime of WSN.