Remotely sensed images usually require segmentation in presence of uncertainty, because of factors like environmental conditions, poor resolution and poor illumination. Therefore, to obtain an efficient algorithm for remotely sensed images is a challenging task. In this paper, a genetic algorithm (GA) based satellite image segmentation using different objective function has been employed for optimal multilevel thresholding. The performance of three different objective functions such as Kapur's, Otsu and Tsallis are compared using GA for optimal multilevel thresholding. Results are analyzed qualitatively and quantitatively both. Compared to other two-thresholding methods, the segmentation results using Kapur's and GA algorithm is found to be most promising, and the computation time is also minimized. From the performance of Kapur's entropy based segmentation, it was found that the genetic algorithm can be efficiently used in multilevel thresholding.