This paper introduces evolutionary generation method of robust corner detectors for rotated images. Previous Harris, SUSAN and FAST corner detectors are highly efficient for well-defined corners, but frequently mis-detect as corners the corner-like edges which are often generated in rotated images. In this paper, we have focused on this challenging problem and proposed using Genetic Programming to do automated generation of corner detectors that work robustly on rotated images. Especially, a well-devised terminal set is proposed based on intensity-related information, several mask sizes, and amount of contiguity of neighboring pixels of similar intensity. This method is then compared to three existing corner detectors on test images and shows superior results.