A satellite constellation is designed to perform its mission with a nominal number of spacecraft. When a reduction in capacity is experienced, for whatever reason, the remaining constellation may be able to restore performance through reconfiguration. In this work we present a general framework that exploits recent efforts in parallel multi-objective evolutionary computation to reconfigure satellite constellations that have suffered the loss of one or more of their vehicles. The framework is illustrated through several loss scenarios for the Global Positioning System constellation. Pareto-hypervolumes are constructed which are the set of solutions that approximate the optimum tradeoff between minimizing cost and risk while maximizing performance. The decision making processes using the high-dimensional data sets is illustrated. The results demonstrate a pragmatic approach to optimization wherein the insights gained from a multi-objective view of the design space tradeoffs allow for informed decision making.