Standard structural design work, is a challenging and time-consuming operation. However, the cost increases significantly since there are so many varied blast characteristics. To solve these problems, it is suggested that structural elements be optimised using algorithms. This work employs two meta-heuristic methods to increase the size of concrete frame constructions. These algorithms identify the structures that have the blast design's lowest weight. Meta-heuristic algorithms are used to tackle optimization problems in many disciplines, including computer science and engineering. To optimize the size of concrete frame structures, two population-based meta-heuristic techniques are applied in this study. The Artificial Bee Colony algorithm and the Cuckoo Search algorithm are these algorithms. Optimization is utilised to reduce the weight of concrete frame constructions while still upholding various displacement constraints. The G + 8 model is vulnerable to blast loading with 2.0 tonnes of TNT while being less than 15 m from the detonation area. The choice to employ the blast load in line with IS:4991-1968 was decided. The Nonlinear analysis is carried out using a piece of software named ETABS. The analysis and design optimization are done in the ETABS by choosing the least weight of structure as the objective function. The analytical tool complies with the displacement and drift constraints set forth in the blast design. Multiple "optimizations" of the modules are possible to the procedure. The algorithms' presumptive blast structure generates the optimal design while lowering expenses. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.