Modern technology encounters difficulties performing DNA sequencing on long DNA sequences. Therefore, longer DNA sequences must be cut into smaller fragments. DNA sequence assembly is the process of combining several short genome sequences to create a longer DNA sequence. This study aims to compare the performance of several population-based metaheuristic algorithms in handling the DNA sequence assembly problem based on computation time, number of contigs, and overlap value. The algorithms used in this study include the Honey Badger Algorithm (HBA), Lévy Flight Distribution (LFD), African Vultures Optimization Algorithm (AVOA), and Particle Swarm Optimization (PSO). Overall, AVOA has the best results where it can produce the most total overlap, where the most overlap is 49952 in the dataset with length 750 and coverage 25. AVOA also has the best efficiency because it has a faster computation time than other algorithms in all datasets. Besides AVOA, PSO produces total overlap and computation time that is not far from AVOA. However, based on the number of contigs, HBA is able to create the least number of contigs, especially on datasets with a length of 750 and coverage of 15, with a total of 6 contigs.