In such a time as the growing DNA and protein sequences of new viruses and complicated anthropological diseases, Multiple Sequence Alignment (MSA) has a vital and crucial mission for countless biological analysis. This needs to be efficient enough to predict the unidentified protein structures and function of a new organism. Finding the best alignment in MSA is a challenging task in biological computation, where it is seen as an NP-Complete optimization problem. This is because the quantity of sequences and their dimensions are increasing at an epidemic rate. The bi-objective based Secretary Bird Optimization Algorithm (BI-SBOA), a meta-heuristic algorithm was used in this work to align biological sequences in order to progress its convergence speed and solving accuracy. Furthermore, two types of objectives can be considered in order to assess the quality of an MSA, such as maximizing the \({Sum}_{pair}\) and \({Total}_{column}\) scores. The BAliBASE benchmark database was used to assess the proposed BI-SBOA technique against the other well-known existing algorithms. The performance metrics for the proposed algorithm were determined using the standard fitness scores of objectives \({Sum}_{pair}\) and \({Total}_{column}\) scores. Based on the end results, it shows that the proposed BI-SBOA method exceeds the performance in average of 13% of the other remaining methods and that is validated by the Wilcoxon signed-rank test results. In addition, the computational costs were assessed by tracking peak memory usage and execution time.