Summary: In many industrial systems, reliability has been considered as an important design measure. In this context, the system reliability maximization subject to performance and cost constraints is well known as the reliability optimization problem. The diversity of system structures, resource constraints, and options for reliability improvement has led to the construction and analysis of several optimization methods, such as dynamic programming, Lagrangian multiplier, and heuristic approaches. On the other hand, a broad class of metaheuristics has been developed for reliability-redundancy optimization. Metaheuristics can overcome many limitations of classical optimization methods and offer a practical way to solve complex optimization problems in reliability engineering. Our study considers one of the latest metaheuristics, harmony search (HS), to solve the reliability-redundancy optimization problems. The HS algorithm was originally inspired by the improvisation process of Jazz musicians. The HS algorithm uses a random search, which is based on random selection, memory consideration, and pitch adjusting. The purpose of this study is to introduce a modified HS approach combined with differential evolution and chaotic sequences to solve optimization problems in reliability engineering. The validity and efficiency of the proposed HS approach are evaluated in two benchmark problems (an overspeed protection system for a gas turbine and a series-parallel system) of mixed integer programming in reliability-redundancy design. Simulation results show that the proposed HS produces promising results in comparison to other optimization methods available in the recent literature for the mentioned benchmark problems.