Case Based Reasoning is a method for solving problems based on previous experience. In some cases large numbers of cases are available representing the experience to draw from. Dealing with large numbers of potentially noisy cases introduces challenges to do with storage capacity and similarity matching but can also lead to sub-optimal solutions. This paper proposes an algorithm for generating a reduced set of highly representative constructed cases, to use as the Smart Case Base in replacement of the original large training data set. The goal of using the reduced set of cases is to address the problems linked to the original Case Base size as well as to maintain or to improve upon generalization accuracy for problem solving. The algorithm makes use of membrane clustering, an evolutionary computing approach for organizing data in large search spaces. The performance of the algorithm has been evaluated using public benchmark data sets.