In this chapter, multi-objective reliability-cost optimization problems have been investigated by utilizing uncertain, vague and imprecise information. During the formulation, a reliability of each component of the system is represented in the form of the triangular interval. The conflicting nature of the objectives is resolved with the help of intuitionistic fuzzy programming technique by recognizing the linear, as well as non-linear membership functions. A crisp model is formulated by using a product aggregation operator to aggregate their expected values. The resultant problem is solved with a gravitational search algorithm (GSA) and compared their results with the particle swarm optimization (PSO) and genetic algorithm (GA). Results are validated through a statistical simulation of the t-test.