This paper deals with the reliability-redundancy optimization problem considering the reliability of each component as an interval valued number that involves the selection of components with multiple choices and redundancy levels which maximize the overall system reliability subject to the given resource constraints arise on cost, volume and weight. Most of the classical mathematical methods have failed in solving the reliability-redundancy optimization problems because the objective functions as well as constraints are non-convex in nature. As an alternative to the classical mathematical methods, heuristic methods have been given much more attention by the researchers due to their easier applicability and ability to find the global optimal solutions. One of these heuristics is genetic algorithm (GA). GA is a computerized stochastic search method of global optimization based on evolutionary theory of Darwin:“The survival of the fittest” and natural genetics. Here we present GA based approach for solving interval valued mixed integer programming in reliability-redundancy optimization problem with advanced genetic operators. Finally, a numerical example has been solved and also to study the effects of changes of different GA parameters, sensitivity analyses have been carried out graphically.