The goal of software architecture optimization is to find architecture candidates that satisfy the expectations of all relevant stakeholders with regard to some quality attributes, e.g., performance, modifiability, or reliability. Quality attributes usually compete with each other, which makes trade-offs inevitable. In the SQuAT project the suitability of distributed search strategies for architecture optimization is investigated. This approach is based on the way human architects would conduct architecture optimization. Related works from the domain of software architecture optimization are usually monolithic and only extendable to a certain degree. The SQuAT approach tries to overcome these drawbacks. This thesis contributes a SQuAT module for the analysis and optimization of software architecture with regard to the quality attribute performance. Therefore, the already existing approach for architecture-based performance optimization PerOpteryx is integrated. In addition, this module gets evaluated with an example system and it is shown that it reaches a similar quality than PerOpteryx. In conclusion, this work is the first step to gain new insights into the applicability of distributed search strategies, modularization of design knowledge, and negotiation techniques for software architecture optimization.