Most of the available modeling and simulation tools for performance analysis do not support model optimization sufficiently. One reason for this unsatisfactory situation is the lack of universally applicable and adaptive optimization strategies. Another reason is that modeling and simulation tools usually have a monolithic software design, which is difficult to extend with experimentation functionality. Such functionality has gained on importance in recent years due to the capability of an automatic extraction of valuable information and knowledge out of complex models. One of the most important experimentation goals is to find model parameter settings, which produce optimal model behavior. In this paper, we elaborate on the design of a powerful optimization component and its integration into existing modeling and simulation tools. For that purpose, we propose a hybrid integration approach being a combination of loose document-based and tight invocation-based integration concepts. Beside the integration concept for the optimization component, we also give a detailed insight into the applied optimization strategies. © 2006, IGI Global. All rights reserved.