For mobile robots, accurate localization is a key issue. Cooperation between multiple robots can, among other benefits, improve their localization capabilities. This thesis investigates the assumption that it is possible to optimize the trajectories of cooperating robots with respect to their localization performance. We present an approach to optimizing entire trajectories for a group of mobile robots that use one another as localization beacons. The cost function we seek to optimize is a measure of localization uncertainty (as opposed to common criteria such as distance travelled or time). Following an overview of previous work on localization of mobile robots, we present our models in an estimation-theoretic framework for cooperative localization. They serve to analyze geometric sensing configurations and to set up the optimization problem. We then apply a Sequential Quadratic Programming method to compute optimized trajectories for selected scenarios. Our findings show that the resulting optimized trajectories yield considerably higher