The conventional Iterative Closest Point(ICP) matching algorithm for laser point clouds had problems of slow convergence and poor robustness, therefore, a point clouds registration method combining multiple optimization methods was proposed. Firstly, point clouds were de-sampled using voxel grid filtering and key points were extracted by ISS operator, then feature extraction algorithm was performed to obtain Fast Point Feature Histograms(FPFH) features of key points, and the multi-core and multi-thread OpenMP parallel processing mode was operated to improve the speed of feature extraction. Then, based on the extracted FPFH features, the Sample Consistency Initial Alignment(SAC-IA) algorithm was used for coarse registration of similar feature points to obtain initial transformation matrix between point clouds sets. Finally, the ICP algorithm was used for fine registration, and the K-D tree nearest neighbor search method optimized by Best Bin First(BBF) was used to accelerate the search speed of corresponding point pairs, and dynamic threshold was set to eliminate the wrong corresponding point pairs, so as to improve the speed and accuracy of point clouds registration. Experimental research on two sets of point clouds shows that the optimized registration algorithm has obvious speed advantages and improves the registration accuracy.