Indoor positioning technology, especially the Received Signal Strength Index(RSSI)-based fingerprinting positioning method, has received extensive attention due to its low cost, wide device support, easy deployment, and low computational overhead. In order to enhance the mapping relationship between RSSI and the actual physical distance and improve the ranging accuracy, this paper proposes an RSSI ranging algorithm based on Improved Grey Wolf Optimization(IGWO) algorithm and Back Propagation Neural Network(BPNN). Compared with Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and classical Grey Wolf Optimization algorithm(GWO), the improved GWO algorithm has significant advantages in positioning accuracy and global search ability. Through experiments, the root mean square error(RMSE) of IGWO algorithm is reduced by 21.3%, 15.7%and 14.6%respectively compared with GWO algorithm, GA algorithm and PSO algorithm. IGWO algorithm shows better positioning performance, and is superior to the traditional methods in accuracy and performance.