The application of adaptation knowledge still poses a major challenge in modern Case-Based Reasoning (CBR) systems. This is especially the case in the subdomain of Process-Oriented Case-Based Reasoning (POCBR), where cases represent procedural experimental knowledge. Current adaptation methods in this field make use of proprietary local search techniques to apply adaptation knowledge, which is inefficient and does not allow exploitation of advanced search and optimization techniques. Therefore, this work presents an approach to transform rule-based adaptation into a planning problem that is solvable with both Constrained Optimization Planning (COP) and Genetic Algorithms (GAs). The results of an experimental evaluation indicate that this transformation is feasible, although it does not achieve significant improvements in terms of adaptation quality without fine-tuning the default search parameters. However, integration into state-of-the-art planning frameworks builds the basis for using a variety of additional features and updates, enhancing the modeling and configuration process.