This paper addresses the analysis and solution of the problem of creating covering arrays (CA) in the context of combinatorial testing. Combinatorial testing, as a fundamental approach in software quality evaluation and testing, helps identify errors and weaknesses in software by combining various variables and conditions. The main goal of this study is to design and create a covering array capable of covering all possible subsets of variables and conditions. To achieve this, new and advanced metaheuristic algorithms are utilized, which have not been previously applied in combinatorial testing. These algorithms leverage evolution-based optimizations and algorithms inspired by natural mechanisms to solve the covering array problem. Specifically, the combination of the Biogeography-Based Optimization (BBO) algorithm with the ROBDD algorithm is introduced as an innovative approach to generate minimal test sequences in covering arrays. These metaheuristic algorithms are recognized for their high capability in solving optimization problems and finding optimal solutions. In this study, these methods are used to generate covering arrays that cover all combinatorial test conditions and provide the best possible results.