Indoor positioning based on the received signal strength index(RSSI) of zigbee had received more and more attention due to its low cost, low hardware power consumption and easy implementation. In order to improve the indoor positioning accuracy of zigbee technology and reduce the adverse effects of environmental factors, this paper proposes an indoor positioning method based on genetic algorithm optimization support vector regression. The algorithm is divided into two stages: offline collection and online prediction. Offline collection is used to establish the fingerprint database, and online prediction is based on the training model for position prediction. Firstly, the all collected data is processed by Kalman filter, and then the penalty parameter , RBF kernel width and loss function variable of support vector regression (GA-SVR) are optimized by genetic algorithm, so that the support vector regression reaches the best position prediction performance. The experimental results in the actual scene show that compared with the PSO-SVR, GS-SVR, SVR and WKNN algorithms, the algorithm has better positioning performance.