Dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection as an effective dimensionality reduction method has attracted more research interests. In this paper, a band selection method for hyperspectral remote sensing images based on subspace partition and particle frog leaping optimization algorithm is proposed. Three new evolution strategies are designed to form a probabilistic network extension structure to avoid local convergence. At the same time, the information entropy of the selected band subset is used as the weight of inter-class separability, and a new band selection criterion function is constructed. The simulation results show that the proposed algorithm has certain advantages over the existing similar algorithms in terms of classification accuracy and running time.