The land cover map is the basis of monitoring changes in open-pit mines. However, owing to the limitation of sensor spectral resolution, the misclassification of pixels is inevitable. To reduce the influence of misclassification on the accuracy of open-pit mine land cover classification (LCC), a feature selection method for open-pit mine LCC based on a multi-feature set using Sentinel-2 images is proposed in this study. First, Sentinel-2 images and shuttle radar topographic mission (SRTM) digital elevation models (DEM) are employed to extract multi-features, including spectral features, topographic features, texture features, and filter features. Then permutation importance (PIMP) feature selection is proposed for selecting optimal features from the multi-feature set. Finally, the results of a practical experiment in Xuzhou City of China are used to verify the validity of the proposed feature selection method. The experimental results show that the multi-feature set can improve the accuracy of open-pit mine LCC and that elevation is the most important feature variable in open-pit mine LCC. Moreover, the PIMP feature selection method can effectively optimize feature combinations to obtain the optimal feature subset. This study provides a useful reference for multi-feature extraction and optimal feature selection in open-pit mine LCC using Sentinel-2 image data.