In this paper we present a new method for automatic feature planning for visual tracking systems employed in visual servoing control of robot manipulators. Such planning of optimal feature sets is of utmost importance in order to ensure accurate and robust execution of general robot tasks using visual servoing. First we introduce a novel platform for simulation and preparation of visual servoing systems. Subsequently we demonstrate how this platform, together with combinatorial optimization techniques and fitness measures which consider several aspects related to the robustness of the tracking system, can be used to plan reliable and information rich feature sets. Finally, we present experiments which compare the performance of a visual servoing system employing the proposed feature planning technique to that of a servoing system based on features selected using traditional methods. These experiments demonstrate that our technique not only improves the robustness of the visual tracking system but also significantly increases the accuracy of the visual servoing control loop.