For remote sensing images with rich content of features, this paper presents a remote sensing image classification algorithm based on twin support vector machine (TWSVM) and multi-feature optimization. Firstly, we extract color feature and shape feature of remote sensing image and introduce the local angular phase (LAP) histogram, which has high texture description ability, as the texture feature. Due to these three kinds of feature represent different emphases of remote sensing image; the reasonable combination of them can be more comprehensive description of the contents of remote sensing image. Secondly, we use kernel principal component analysis (KPCA) to reduce the dimension of every kind of feature, and construct reasonable feature space based on different weights obtained by the distribution of feature space. Finally, the remote sensing image samples classification and test is completed in the TWSVM model that has better classification performance. Experimental results on the USGS test-set show that, the average classification accuracy of the proposed algorithm is reached to 93.7% compared with three popular methods. Compared with the highest classification accuracy of single feature classification algorithm, the average classification accuracy of the proposed algorithm has been improved by 16%, 14.5% and 9.2%.