This paper deals with the superpixel segmentation problem using a powerful global optimization technique: Differential Evolution. The algorithm mimics the process of nature evolution to realize efficient optimization, and it poses no restrictions on the form of objective functions. This way, we develop a novel and comprehensive objective function considering both local and global costs in the segmentation, including within-superpixel error, boundary gradient, a regularization term. The proposed method can produce superpixels in a computational time linear to the image size. Experimental results validate the competitive performance of our algorithm in terms of boundary adherence and segmentation capability.