The idea that the efficient usage of all necessary production resources has not only an ecological im-portance but is also an overwhelming economical competitive factor becomes accepted since the 1980s. Modeling and simulation with integrated parameter optimization is used routinely to improve process performance. In engineering a well known environment for this task is the MATLAB/Simulink programming system. Using this or similar, established techniques only model parameter of a single model structure is optimized. Model structure is considered to be fixed as the relationships between model elements are defined during model development. Until now no tools and methods are known which can optimize product design and production processes utilizing all exist-ing degrees of freedom. As process performance is optimized it may be necessary to redesign the model struc-ture. The redesign is normally carried out manually by an analyst but not automatically by the optimization method. This suboptimal combination of automatic parameter optimization and manual structure changes leads to a time consuming and error-prone optimization task. The system theoretical approach of the System Entity Structure/Model Base framework (SES/MB) is able to define alternative model structures and parameter sets in a single meta-model, called System Entity Structure (SES). Moreover, atomic models are stored in a model base (MB). Using both, SES and MB, it is possible to generate modular, hierarchical models with different structures and parameters. Evolutionary Algorithms are a subtopic of Artificial Intelli-gence that are involved in combinatorial optimization problems. These algorithms are based on ideas inspired by biological evolution. They often perform well for many problem types because they do not make assumptions about the problem specific search space. The research reported in this paper details an approach providing optimization through automatic reconfiguration of both: model structure and model parameters. An evolutionary algorithm based optimization method is assisted by an SES/MB based model management. It searches for an optimal solution with repeated, combined model parameter and model structure changes resulting in a combined parameter and structure optimized model.