Image segmentation is the first stage of processing in many practical computer vision systems. While development of particular segmentation algorithms has attracted considerable research interest, relatively little work has been published on the subject of their evaluation. In this paper we propose the use of the Pareto front to allow evaluation and comparison of image segmentation algorithms in multi-dimensional fitness spaces, in a manner somewhat analogous to the use of receiver operating characteristic curves in binary classification problems. The principle advantage of this approach is that it avoids the need to aggregate metrics capturing multiple objectives into a single metric, and thus allows trade-offs between multiple aspects of algorithm behavior to be assessed. This is in contrast to previous approaches which have tended to use a single measure of ``goodness'', or discrepancy to ground truth data. We define the Pareto front in the context of algorithm evaluation, propose several fitness measures for image segmentation, and use a genetic algorithm for multi-objective optimization to explore the set of algorithms, parameters, and corresponding points in fitness space which lie on the front. Experimental results are presented for six general-purpose image segmentation algorithms, including several which may be considered state-of-the-art.