The complexity and physical distribution of modern ac- tive safety, chassis and powertrain automotive applications requires the use of distributed architectures. Complex func- tions designed as networks of function blocks exchanging signal information are deployed onto the physical HW and implemented in a SW architecture consisting of a set of tasks and messages. The typical configuration features priority- based scheduling of tasks and messages and imposes end- to-end deadlines. In this work, we optimize the task place- ment and the signal to message mapping and we automate the assignment of priorities to tasks and messages in order to meet end-to-end deadline constraints and minimize la- tencies. This is accomplished by leveraging worst case re- sponse time analysis within a mixed integer linear optimiza- tion framework. Our approach is applied to an automotive case study to prove its feasibility.