We have been investigating the science performance for different gravity mission constellation architectures using smallsats. Small satellite systems are increasingly being used in scientific missions, due to their increase in affordability and improvement in performance over the past years. A small satellite constellation of GRACE-like pairs or other intersatellite ranging configurations would allow for improved spatial and temporal resolution as well as allowing for a high inherent system redundancy and a lower overall cost. Additionally, such a mission architecture would be more robust to failure since a constellation is insensitive to single point failures and individual satellites can be replaced at a lower cost.
The design of such a mission architecture is not straight forward due to the vast search space that needs to be considered. In this work we make use of a multi-objective evolutionary algorithm that is population-based and metaheuristic based on Darwinian theory in order to identify future GRACE-like constellations that are optimized to retrieve sub-monthly time-varying gravity field events.