This chapter presents an alternative multiobjective image segmentation algorithm which aims to find the suitable solutions that balance between two different objectives. The proposed method depends on the improvement of the ability of the multi-verse optimization algorithm using the opposite based learning method. The proposed approach, called MOGWO, uses the Otsu and Kapur function to determine the approximate Pareto-optimal set of solutions. There are a set of experiments are performed using seven images to evaluate the performance of the proposed method as a multiobjective segmentation method. In addition, it is compared with other multiobjective meta-heuristics such as NSGA-II, MOPSO, and MOEAD. The comparison used the PSNR and SSIM to evaluate the segmented images and Hypervolume to assess the solutions. The experimental results show that the proposed method outperforms the other multiobjective algorithms based on the performance measures.