In the design of critical combinations and complex integrations of large engineering systems, their reliability, availability and maintainability (RAM) analysis of the inherent processes in the system and their related equipments are needed to be determined. Although there have been tremendous advances in the art and science of system evaluation, yet it is very difficult to assess these parameters with a very high accuracy or precision. Basically, this inaccuracy in assessment stems mainly from the inaccuracy of data, lack of exactness of the models and even from the limitations of the current methods themselves and hence management decisions are based on experience. Thus the objective of this chapter is to present a methodology for increasing the performance as well as productivity of the system by utilizing these uncertain data. For this an optimization problem is formulated by considering RAM parameters as an objective function. The conflicting nature between the objectives is resolved by defining their nonlinear fuzzy goals and then aggregate by using a product aggregator operator. The failure rates and repair times of all constituent components are obtained by solving the reformulated fuzzy optimization problem with evolutionary algorithms. In order to increase the performance of the system, the obtained data are used for analyzing their behavior pattern in terms of membership and non-membership functions using intuitionistic fuzzy set theory and weakest t-norm based arithmetic operations. A composite measure of RAM parameters named as the RAM-Index has been formulated for measuring the performance of the system and hence finding the critical component of the system based on its performance. Finally the computed results of the proposed approach have been compared with the existing approaches for supremacy the approach. The suggested framework has been illustrated with the help of a case.