Traditional manifold learning methods assume that face data may reside on one single manifold, but data from different classes may reside on different manifolds of possible different intrinsic dimensions, thus the assumption of single manifold may affect the learning of the actual distribution relationship of the image data in the high dimensional space. In this paper, a multiple manifold learning algorithm based on multiple neighborhood preserving embedding(M-NPE) was proposed to find a low-dimensional embedding for data lying on multiple manifolds. First, the manifolds of different classes were learned by NPE for each class separately, and the low dimensionality coordinates and mapping matrix of the data was obtained. The genetic algorithm (GA) was then employed to obtain the nearly optimal dimensionality of each face manifold from the classification viewpoint. Classification was performed under a criterion that is based on the minimum reconstruction error on manifolds. The experimental results on both Extended Yale B and CMU PIE large-scale face database verified the effectiveness of the algorithm.