In modern vehicles, each system must meet tough demands to fulfill the many different attribute requirements, design constraints and manufacturing limitations. It becomes difficult and time-consuming to find an optimal and robust design using a traditional engineering process. Volvo Cars has for several years been using Multi-Disciplinary Optimization, MDO, that basically shows the customer attributes levels, such as NVH, ride comfort, and driveability as a function of different parameter configurations. This greatly facilitates project team understanding of the limitations and possibilities of the different systems, and has become a key enabler to achieving a good balance between different attributes. Traditionally, this type of comprehensive Design of Experiments (DOE) optimization demands huge time and computer resources. Frequently, experimental designs will not fulfill manufacturing limitations or attribute targets, making this decision process slow, tedious, and fruitless.In this paper, a systematic Multi-Disciplinary Optimization process is presented that considerably reduces the required resources and time. It uses a naturalistic way of choosing suitable designs that fulfill manufacturing limitations and a well-defined attribute balance. Project teams quickly get a structured view of the limitations for a specific system regarding the attribute requirements and, as a consequence, require less prototype material and time for real-world testing.