Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations; however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area.