In an application of side-attack explosive hazard (SAEH) detection, one challenge is detecting occluded targets. Forward-looking ground penetrating radar (FLGPR) is a sensor that has the capability to handle this type of problem since this frequency can penetrate occlusion and detect the explosive. However, the explosive can be concealed by multiple types of objects along the side of the road, such as rocks, vegetation, trash, etc. In FLGPR imaging space, these clusters of occlusion come in many shapes and sizes. The log Gabor descriptor, which has the advantage of separating different types of textures, is a potential solution for the aforementioned issue. However, in order to fully utilize the log Gabor filter bank, there are many parameters that need to be handcrafted for each specific problem. Since the possibility of designing a filter bank is endless, brute-force search is not possible. An additional element to be considered is the size of the filter bank, since a large filter bank requires more computation time. In this paper, we propose the use of multi-objective optimization based on an evolutionary algorithm to streamline the process of designing a log Gabor filter bank. The objectives of the optimization are to design a filter bank that can separate the explosive hazards from false alarm hits while keeping the size of the filter bank to a minimum. The data we use to validate our proposed method was collected on arid lanes by a MIMO FLGPR system on board of the U.S. Army prototype vehicle. The experiments are designed to measure the improvement of the optimization over the manual tuning and to demonstrate the impact of the optimization’s parameters on the performance of the filter bank.