Abstract: The inversion problem of magnetotelluric is non-linear. If the linear inversion method is used to linearize the non-linear problem,it is easy to fall into the local minimum and the non uniqueness of the inversion results is serious. At present,the single nonlinear intelligent algorithm is easy to fall into local minimum and low efficiency in inversion. In order to solve this problem,this paper designs a nonlinear hybrid intelligent algorithm combining particle swarm optimization,differential evolution algorithm and Nelder-Mead optimization algorithm. At the same time,the hybrid intelligent algorithm makes use of the ability of particle swarm optimization and differential evolution algorithm for global optimization. At the same time,it also uses the Nelder-Mead optimization algorithm for further local development of the optimization results,which solves the problems of linear algorithm and single nonlinear intelligent algorithm easily falling into local minimum and low efficiency of optimization. In the process of magnetotelluric inversion,regularization theory is introduced to constrain the objective function to avoid non-uniqueness of the inversion results. The damped particle swarm optimization,differential evolution and the hybrid intelligent algorithm proposed in this paper are respectively applied to the typical multidimensional function optimization and theoretical model. The results show that the hybrid intelligent algorithm in this paper has high efficiency and good robustness compared with damped particle swarm optimization algorithm and differential evolution algorithm. Finally,the hybrid intelligent algorithm is applied to the inversion of a measured point data of Jilin huapichang geothermal field,and the inversion results are compared with the inversion results of the traditional inversion algorithm,which shows that the inversion results of the algorithm in this paper are more consistent with the actual exploration results,which proves that the hybrid intelligent algorithm has a high accuracy in the inversion of magnetotelluric measured data.