Gabor Filters have been extensively used to solve the texture-based image segmentation problem, following the filter bank and filter design approaches. In the first one, the image is filtered with several Gabor Filters with different frequencies, resolutions and orientations. The parameters of these filters are fixed and can be suboptimal for a particular processing task. The techniques based on filter design, on which this work is focused, permit to “tune” the parameters of the filter. This work proposes the use of two optimization algorithms (Guided Random Search and Particle Swarm) in this tuning process, showing good results in texture classification tests.