In this paper, an Active Cooperative Localization system for Quadrotor Unmanned Aerial Vehicles is developed. The optimal trajectories are determined by minimizing the uncertainty in position estimation by Extended Kalman Filter. In this system, a piecewise polynomial parameterization of trajectories is adopted for the optimizer, and the underlying state estimator is updated with appropriate models of sensors and quadrotor dynamics. This system is verified in extensive simulations in the scenario of a team of quadrotors with heterogeneous GNSS capabilities. These simulations answer an open question, showing that solving for trajectories by minimizing Kalman covariance computed in a noiseless environment is reasonable and that the optimized trajectories offer visible reductions in positioning uncertainty in the presence of noise.