The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest. This is challenging without any prior knowledge about the object that is being extracted from the scene. We previously proposed a method of segmentation that uses the classification subsystem as an integral part of the segmentation, which provides contextual information regarding the objects to be segmented. Our approach integrated segmentation and classification in a manner analogous to wrapper methods in feature selection. We initially perform low-level segmentation to label the image as a set of nonoverlapping blobs. We then use the wrapper framework to select the blobs that comprise the final segmentation based on the classification performance of the wrapper. In this paper, the process of combining the blobs and then evaluating these combinations is performed with a genetic algorithm. We show the performance of the Genetic Algorithm based wrapper segmentation on real-world complex images of automotive vehicle occupants, where our overall classification accuracy is roughly 88% and the resultant segmentations are extremely accurate.