Nowadays, vision-based inspection systems are present in many stages of the industrial manufacturing process. Their versatility, which permits us to accommodate a broad range of inspection requirements, is, however, limited by the time consuming system setup performed at each production change. This work aims at providing a configuration assistant that helps to speed up this system setup, considering the peculiarities of industrial vision systems. The pursued principle, which is to maximize the discriminating power of the features involved in the inspection decision, leads to an optimization problem based on a high-dimensional objective function. Several objective functions based on various metrics are proposed, their optimization being performed with the help of various search heuristics such as genetic methods and simulated annealing methods. The experimental results obtained with an industrial inspection system are presented. They show the effectiveness of the presented approach, and validate the configuration assistant as well.