A point set registration algorithm based on symmetric Kullback-Leibler (SKL) divergence is proposed. Each point in the point set is represented as a Gaussian distribution. The Gaussian distribution includes the location information of the point and the influences from surrounding points. The whole point set is modeled as a Gaussian mixture model (GMM). The registration problem of two point sets is thus formulated as the minimum value solution of SKL divergence between two GMMs. The genetic algorithm is used for optimal solution. The experimental results show that the proposed algorithm is robust to noise, outliers, and missing points, and achieves good registration accuracy.