Designing a vehicle chassis involves meeting numerous performance requirements related to various domains such as Durability, Crashworthiness and Noise-Vibration-Harshness (NVH) as well as reducing the overall weight of chassis. In conventional Computer Aided Engineering (CAE) process, experts from each domain work independently to improve the design based on their own domain knowledge which may result in sub-optimal or even non-acceptable designs for other domains. In addition, this may lead to increase in weight of chassis and also result in stretching the overall product development time and cost. Use of Multi-Disciplinary Optimization (MDO) approach to tackle these kind of problems is well documented in industry. However, how to effectively formulate an MDO study and how different MDO formulations affect results has not been touched upon in depth.This study implements various MDO formulations on an established SUV chassis frame for further weight reduction and performance improvement. Results from different MDO formulations are compared and insights are provided into ways of formulating an MDO problem in order to achieve desired results. How the choice of optimization algorithm, number of objectives and constraints etc. affect MDO results is also discussed. 18 component thicknesses are selected based on domain understanding as design variables (DVs) for optimization. Various attributes/load-cases considered during optimization are -bending and torsional stiffness for Durability, global mode shapes from modal analysis for NVH, energy absorption and displacements at specific locations during frontal offset crash for crashworthiness. As a first step, a Design of Experiment (DoE) study is performed for each domain individually and results are analysed to identify conflicting targets. Response Surface (RSM) based optimization method is selected for optimization studies considering time and resource availability. Commercial software package, modeFRONTIER, is used as a process integration, design optimization and data analysis tool. Optimal designs from all MDO studies are compared with each other to evaluate effectiveness of formulations.