A point cloud registration algorithm based on cosine similarity (PCR-CS) is proposed. This algorithm mainly solves the problem of point cloud rigid registration, which involves finding the rotation matrix R and the translation matrix T of the point cloud registration to realize registration between the original point cloud P and the target point cloud Q. In the proposed algorithm, first, the two points clouds to be registered are decentralized and the cosine similarity of the point clouds is studied. Then, the two three-dimensional point clouds to be registered are projected onto the XY plane and rasterized on the XY plane. The data points on the statistical grid form the statistical matrices S_P and S_Q. Moreover, the differential evolution algorithm is used to find the optimal R under the condition of the cosine similarity of the two points clouds to achieve point cloud registration. Finally, the center point is used to calculate T. Experiment results show that compared with other algorithms, the proposed algorithm has higher registration accuracy. In addition, even when the point cloud data are accompanied by noise or missing data, it can achieve good registration results.