Most real world processes are heavily influenced by environmental factors, which are referred to as the context of a process. Thus, the consideration of context is proposed within the research field of Business Process Modeling. Most existing context-aware modeling approaches consider context only in terms of static information like, for instance, the location where a process is per-formed. However, context information like the weather could change during the conduction of a process, which we will denote as non-static context. In order to increase the flexibility concern-ing environmental influences in general and especially context-related events of context-aware processes, we present an approach for the automated planning of context-aware process models that considers static and non-static context. We therefore propose an extended state transition system in order to represent context information in terms of context variables and consider pro-cess exogenous changes of these context variables through context signals and receive context actions. Further, to ensure a correct, complete and time efficient construction of context-aware process models, a planning approach is used to support modelers by means of an algorithm. To demonstrate the feasibility of our approach we mathematically evaluated the algorithm and ap-plied it to real world processes.