Oversubscribed scheduling problems require removing tasks when enough resources are not available. Prior AI approaches have mostly been constructive or repairbased heuristic search. In contrast, we have found a genetic algorithm (GA) to be the best approach to the overconstrained problem of Air Force Satellite Control Network scheduling. We present empirical results that elucidate sources of difficulty in the application and partially explain why the GA is well suited to this problem. We show that the task interaction compels changes involving many tasks simultaneously and the GA appears to be learning domain specific patterns in the data.