The craze for the Advanced Driving Assist Systems (ADAS) became major these last years. It leads the car manufacturers to constantly improve their functionalities and to distribute them to a large segment of consumers. Hence, the ADAS has to be adaptable to all vehicle models and above all approved by the drivers. Moreover, since safety of the passengers is at stake, performance and robustness need to be guaranteed. Designing these new systems is more and more time-consuming even though costs must remain low. To cope with this contradiction, the main objective of my Ph. D. thesis is to construct a generic tuning tool for the ADAS’s control laws, including an environment model, and based on multi-objective optimization as well as sensitivity and robustness control quality criteria. This tool has to be flexible and systematic, adapting easily to the many performance and reliability constraints and to different cars. The approach envisaged is to consider a cost function and constraints having strong physical sense although mathematically formalized to be tractable by the optimization process. Besides, we aim to propose an economic and intuitive parametrization to the ADAS functionality considered, facilitating thus the comprehension of the result.