The robustness of robot calibration with respect to sensor noise is sensitive to the manipulator poses used to collect measurement data. In this paper we propose an algorithm based on a constrained optimization method, which allows us to choose a set of measurement configurations. It works by selecting iteratively one pose after another inside the workspace. After a few steps, a set of configurations is obtained, which maximizes an index of observability associated with the identification Jacobian. This algorithm has been shown, in a former work, to be sensitive to local minima. This is why we propose here meta-heuristic methods to decrease this sensibility of our algorithm. Finally, a validation through the simulation of a calibration experience shows that using selected configurations significantly improve the kinematic parameter identification by dividing by 10-15 the noise associated with the results. Also, we present an application to the calibration of a parallel robot with a vision-based measurement device.