The robustness of robot calibration with respect to sensor noise is sensitive to the manipulator poses used to collect measurement data. The present paper proposes an algorithm based on a constrained optimization method which allows one to choose an set the measurement configurations. It works by selecting iteratively one pose after another inside the workspace, and after a few steps a set of N configurations is obtained which maximizes an index of observability associated to the parameter Jacobian. Our algorithm converges to a set of poses that are localized at the. boundary of the articular workspace. Finally, a validation through simulation of a calibration experience shows that selected configurations improve significatively the kinematic parameters identification by dividing 10 to 15 the noise associated to the results.