The demand for computing power and performance in real-time embedded systems is continuously increasing since new customer requirements and more advanced features are appearing every day. To support these functionalities and handle them in a more efficient way, multi-core computing platforms are introduced. These platforms allow for parallel execution of tasks on multiple cores, which in addition to its benefits to the system's performance introduces a major problem regarding the timing predictability of the system. That problem is reflected in unpredictable inter-core interferences, which occur due to shared resources among the cores, such as the system bus. This thesis investigates the application of different optimization techniques for the offline scheduling of tasks on the individual cores, together with a global scheduling policy for the access to the shared bus. The main effort of this thesis focuses on optimizing the inter-core data propagation delays which can provide a new way of creating optimized schedules. For that purpose, Constraint Programming optimization techniques are employed and a Phased Execution Model of the tasks is assumed. Also, in order to enforce end-to-end timing constraints that are imposed on the system, job-level dependencies are generated prior and subsequently applied during the scheduling procedure. Finally, an experiment with a large number of test cases is conducted to evaluate the performance of the implemented scheduling approach. The obtained results show that the method is applicable for a wide spectrum of abstract systems with variable requirements, but also open for further improvement in several aspects.