This chapter discusses an efficient algorithm on the basis of genetic algorithm and particle swarm optimization for solving mixed-integer nonlinear reliability optimization problems in complicated reliability systems with ambiguous/imprecise parametric values. Development of a technological system, the decorative pattern of the system, and system reliability are indispensable measures in industries, especially in complicated manufacturing systems, namely, smartphone, laptop, digital laboratory instruments, and smart watch. The purpose of the reliability optimization problem is to upgrade the reliability of a system within given budget constraints. Reliability redundancy allocation problem aims at finding the number of redundant components and also the reliability of each component to maximize the system reliability and/or minimize the system cost/system volume/system weight under requisite budget constraints. The reliability of each component and other parameters related to this problem have been considered to be a triangular fuzzy number, and reconstruction of equivalent problem has been done to crisp optimization problem with the help of defuzzification method.