The software part of an automotive embedded system continues to increase significantly. It enables the development of new functionalities and it may improve the quality and comfort of driver assistance functions. However, the design of such functions becomes a complex task involving networked ECUs (Electronic Control Unit), several sensors/actuators and a set of embedded networks.The introduction of Model-Based Development (MBD) in the automotive field promised to improve the development process by allowing continuity between requirements definition, system design and the distributed system implementation. Further, the definition of AUTOSAR consortium standardized the design of such automotive embedded system by allowing the portability of software functions on the hardware architecture and their reuse. It defines a set of rules and interfaces to design, interconnect, deploy and configure a set of application software components (SWCs).However, designing an embedded system according to AUTOSAR standard necessitates the configuration of thousands of parameters and requires several software allocation decisions. Each decision may influence the system performance and also the development cost. This architectural complexity leads to a large design decision space which is difficult to be explored without using an analytical method or a design tool. For example, mapping software components (SWCs) to ECUs may affect the system performance.Actually, this phase of configuration and software allocation is performed manually using engineering and system architect knowledge. AUTOSAR provide a methodology for the software development of an Electricals/Electronics (E/E) system. However, this method doesn't guide the designer to deploy and bring a high-level software function onto a set of SWCs and then SWCs to ECUs.In this paper we present a model-based methodology to optimize software allocation and component configuration of an AUTOSAR system. This methodology relies on a multi-objective evolutionary algorithm which is characterized by its composability and speed performance. This algorithm is combined with a model-based system analysis engine permitting to evaluate system performance objectives. The objectives considered here are the CPU load, network load and functions response times.