This study proposed a differently scaled point cloud registration algorithm based on artificial bee colony optimization that can improve the accuracy and efficiency of differently scaled point cloud registration. The scale scaling factor, together with the three-dimensional rotation and translation parameters, was introduced as the variables to be solved in the registration process, and the artificial bee colony optimization method was used to optimize the solution. Furthermore, the proposed algorithm improved the Euclidean distance objective function based on the normalized scale factor, which eliminated the errors caused by optimizing the scale scaling factor to effectively improve the stability of the registration algorithm. Compared to currently employed methods, the proposed algorithm improves the accuracy and efficiency in different model registrations. The experimental results demonstrate that the proposed algorithm utilizes the excellent global optimization ability of the artificial bee colony optimization method and can therefore effectively realize the high-precision and fast registration for differently scaled point clouds.