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Because the DV-Hop localization accuracy of distance-independent WSN node localization algorithm is not high, intelligent optimization algorithm is introduced to improve the localization accuracy effectively, but the number of iterations is too large and the energy consumption of nodes is relatively high. When there are fewer anchor nodes and shorter communication radius, the traditional intelligent optimization algorithm is difficult to take effect. In view of this situation, a two-stage differential evolution location optimization algorithm is proposed.The simulation experiment is designed to randomly distribute 100 wireless sensor nodes in a square area of 100m x 100m, DV-Hop algorithm is used to locate roughly in the first stage, then differential evolution algorithm is used to optimize the location in the second stage, in order to compare the performance of various algorithms under low energy consumption (few iterations), the optimization process only iterates for 10 generations,and finally the coordinates of nodes are obtained. The experimental results show that the algorithm can achieve better positioning accuracy and stability. Under the condition of few iteration algebras, the algorithm achieves satisfactory positioning accuracy and better stability under the special circumstances of sparse anchor nodes and short communication radius.