At present, intelligent optimization algorithms have become a hot topic in many research directions, but there are few studies and articles applying them to indoor TDOA positioning. Therefore, this article first uses six intelligent optimization algorithms, namely WSO, CSA, SOA, WOA, GWO, and SSA, for indoor two-dimensional TDOA positioning. The performance of these algorithms in the field of indoor positioning is compared and analyzed, and the positioning error is compared with the traditional Taylor algorithm; Next, use the SOA algorithm to optimize the BP neural network, use SOA-BP for positioning, and compare the positioning error with the basic BP neural network. The experiment shows that the six intelligent optimization algorithms used in this article have good performance in the field of indoor positioning. The effects of each intelligent optimization algorithm are similar, with an average positioning error of about 0.44m, which is about 9.2% higher than the traditional Taylor algorithm; The positioning error of SOA-BP is reduced by more than 30% compared to the basic BP neural network.