In robotic manipulation the interaction between robot and environment is an essential part of the task. In order to deal with impacts, friction and constrained motion, the proposed methods are not based on elaborate control schemes. Rather, a model-based optimization approach is suggested, which relies on a detailled dynamic model of the manipulator itself, the process dynamics of the task and the interactions between those two. These models are used to define an optimization problem, which is then solved using numerical programming methods. It is illustrated with an assembly task, namely mating a pair of snap fits. Utilizing a pre-optimized dynamic response behavior of the robot as developed in former work, [6], we plan trajectory and design parameters of the parts to be assembled, which influence the process dynamics and thus the external excitation of the manipulator dynamics. The expected advantage in industrial applications is a relatively easy implementation, because performance can be improved by simply adjusting 'external' parameters as path and geometry. Particularly, no modifications of the control architecture are required. Application of the proposed approach to a snap joint under practical constraints can reduce the cycle time by more than 45%. This is achieved with strains on robot and mating parts equal to those encountered in a predefined nominal case.