Abstract : The aim of optimization is to achieve the most favorable design considering a prioritized set of criteria or constraints. In the realm of engineering advancements, it is crucial for engineers to explore beyond conventional methods. While traditional approaches may provide comfort and familiarity, designers must recognize the increasing significance of optimization methods. These methods play a crucial role in saving both time and money while simultaneously reducing errors. This research represents a modest stride toward comprehending the advantages that optimization can bring to structural design. It aims to contribute to one of the primary goals of every engineer: designing structures that are not only safe but also economically and environmentally sustainable. To accomplish this, design optimization algorithms such as ABC (The Artificial Bee Colony), TLBO (Teaching Learning Based Optimization), and DE (Differential Evolution) were employed to determine the optimal cross-sectional dimensions of columns following the AISC-LRFD design provisions. The software tool ETABS was utilized to obtain the frame's response to external loads, and the optimization iterations were executed using MATLAB. Altering the cross-sectional properties of the members in the 3-D steel frame leads to changes in their performance under external loads, necessitating a new analysis. This is facilitated through the use of the Application Programming Interface (API) feature of ETABS, establishing a link between the coded MATLAB program and ETABS for bidirectional data exchange. Iterations continue until the convergence criteria are satisfied. It was found that the utilized optimum design algorithms were able to optimize the weights and reduce them to the minimum possible sections. It was also found that the proposed TLBO and DE are efficient and better than the standard ABC in terms of the convergence history and providing the best results.