The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral–Spatial features, denoted as OSS-MSSC, is proposed in this paper. In the proposed method, the multi-scale superpixels are firstly generated to capture the local spatial structures of the ground objects with various sizes. Then, the normalized difference vegetation index and extend multi-attribute profiles are introduced to extract the spectral–spatial features from the multi-spectral bands of the image. To reduce the redundancy of the spectral–spatial features, the crossover-based search algorithm is utilized for feature optimization. The pre-classification results at each single scale are, therefore, obtained based on the optimized spectral–spatial features and random forest classifier. Finally, the ultimate classification is generated via the majority voting of those pre-classification results in each scale. Experimental results on the Gaofen-2 image of Qingdao and WorldView-2 image of Hong Kong, China confirmed the effectiveness of the proposed method. The experiments verify that the OSS-MSSC method not only works effectively on the homogeneous regions, but also is able to preserve the small local spatial structures in the high-resolution remote sensing images of coastal cities.