In periods of a growing relevance for automated driving, dynamic simulators present an appropriate simulation environment to repeat driving scenarios with good fidelity. A realistic replication of the driving dynamics is an important criterion to immerse persons in virtual environments provided by the simulator. Motion Cueing Algorithms (MCA) determine the simulator's input subject to the driving dynamics demand. The main limitations come from technical restrictions of the simulator's actuators. Typical dynamical simulators consists of a hexapod, exhibiting six degrees of freedom (DoF) to reproduce the vehicle motion in all dimensions. As its workspace dimensions are limited, an approach is to expand the simulator with redundant DoF by additional motion systems. This work introduces a global optimization scheme which is able to find an optimal motion for a driving simulator exhibiting three redundant degrees of freedom. The simulator consists of a tripod with three DoF in longitudinal-, lateral- and yaw-direction as well as a hexapod mounted on top of the tripod's motion platform. The MCA is based on a model predictive control algorithm which solves a global optimization problem at each sampled time instance. A cost function minimizes the difference between a reference trajectory and the simulator's motion subject to the actuators constraints. Due to the non-linear kinematic relations between workspace and actuators space of both motion systems, a linearization approach is shown to limit the actuator restrictions on position, velocity and acceleration level over a finite time horizon. We applied the proposed scheme to the real simulator and evaluated the method for various driving scenarios.