Solving the spatial optimization problems can be a complex and difficult task, since it has to handle some high-dimension, non-linear, complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. In this study, a novel framework used for spatial optimization based on clonal selection algorithms, which are the most popular immune algorithms in geoscience, is proposed. Then, the spatial optimization platform was designed based on the architecture of “plugins – platform”, and some key technologies about the developing of immune operator plugins and spatial optimization application plugins were described. Based on the standard APIs provided by this platform, researchers can develop their own problem-specific application plugins to solve the practical problems or to implement some advanced immune operators into the platform to improve the performance of the algorithm. Finally, the functionality, reusability and extensibility of platform were tested by using the Traveling Salesman Problem as a benchmark testing. Experiments show that, the platform is capable of solving various optimization problems, and it is expected to bridging the gap between the immune algorithm researchers, geographers and decision makers.