A well-known problem in Computer Vision is the detection of interest points, which is important for tasks such as object detection, image matching, object recognition, 3D reconstruction, tracking, and image registration. This work proposes the use of Grammatical Evolution to perform the automated design of Interest Point Detectors, in search of the optimum detector, for image matching. Interest Point Detectors were evolved with base on low level operations such as Gaussian filters, Laplacian filters, etc. and intensity operations. Images with planar transformations (rotation, translation, and scaling), that simulate homographies between the original and transformed images, were used to guide the evolutionary search, based on the repeatability rate computed on the detected points. With repeatability rates generally above 80% and up to 98%, the results obtained are comparable to, and in specific cases superior to, detectors used by state-of the-art methods, such as SIFT, FAST and ORB. Additionally, some novel, robust interest point detectors are identified (average 87.6% repeatability rate), demonstrating the effectiveness of the proposed approach in challenging scenarios. This work shows that Grammatical Evolution is an efficient method that allows the automated design of effective algorithms without having to perform the cumbersome process of trial-and-error by hand.