Segmentation of satellite images is an important step for the success of the object detection and recognition in image processing. Segmentation is the process of dividing the image into disjoint homogeneous regions. There are many segmentation methods and approaches, the most popular are clustering methods and approaches such as Fuzzy C-Means (FCM) and K-means. The success of clustering methods depends strongly on the selection of the initial spectral signatures. Normally, this is done either manually or randomly, in either case the outcome is unpredictable. In this paper an unsupervised method based on Multi-Objective Genetic Algorithm (MOGA) for the selection of spectral signature from satellite images is described. The new method works by maximizing the number of the selected pixels (minimize over-segmentation) and by minimizing the difference between these pixels and their spectral signature (maximize homogeneity). Experimental results are conducted using a high resolution SPOT V satellite image, the collected spectral signatures, and the K-means clustering algorithm. The verification of the segmentation results is based on a very high resolution satellite image of type QuickBird. The spectral signatures provided to K-means by MOGA increased the speed of clustering to approximately 4 times the speed of the random based selection of signatures. At the same time MOGA improved the accuracy of the results of clustering using K-means to more than 10 %.