This paper focuses on approaching reliability–redundancy allocation problems, using hybrid schemes that consist of individual nature inspired algorithms. The aim is to investigate if hybridization is an efficient way to approach problems with multiple goals. Therefore, known algorithms that have been successfully applied to reliability and redundancy allocation problems in literature are implemented as part of hybrid schemes in this study. The idea behind that is to study whether an efficient hybrid scheme is the outcome of the hybridization of (individual) efficient algorithms. The performance of the nine proposed schemes and the individual algorithms is tested on ten well-known artificial and real-world case studies, from the field of reliability engineering. The numerical results are compared to others from literature highlighting the efficiency of the proposed hybrid schemes and consequently, support the hypothesis that hybridization can enhance the performance of optimization methods. The experimental results demonstrate that among the nine hybrid schemes, the one consisting of Bat Algorithm and Firefly Algorithm shows the best performance.