Advances in the technologies of sensors and lightweight robots increasingly enable direct physical interaction of humans and robots. This so-called human-robot collaboration is supposed to offer more flexibility in production processes, as opposed to fully automated processes. An efficient human-robot collaboration therefore needs to be well-coordinated. The aim of this Master Thesis is to develop a mathematical optimization model for a production process based on the real-world setting of printed circuit boards, which coordinates the distribution of tasks between humans and robots, such that all specified restrictions are met and the total completion time of the process is minimized. First of all, the thesis addresses the general problem of scheduling tasks. Different problems are classified and the use-case considered is identified as a Resource Constrained Project Scheduling Problem with Multiple Processing Modes (MRCPSP). Possible exact and heuristic solution methods of MRCPSP are given. Additionally, some of the problems discussed in the human-robot collaboration literature are described. On the one hand, this should give the reader a broader understanding of the subject matter. On the other hand, this should provide an overview of possible alternative approaches to solving the given problem. The mathematical optimization model is then explained in detail and possible model extensions are proposed. The results obtained are presented using several modified scenarios. They are then compared with the solutions of a heuristic method fitted to the practical problem. Finally, the limits for the application of the optimization model are discussed.