As e-commerce continues to expand, AIGC-based advertising has gained momentum, making efficient and cost-effective ad placement a key priority for businesses. Because placement decisions are shaped by many interacting factors, this study proposes a research framework that formulates AIGC advertising placement as a multi-objective optimization problem and solves it using a locust optimization algorithm. The model defines objective functions, constraints, and fitness measures aligned with practical operating conditions, and then computes an optimal placement strategy through iterative optimization. To assess the approach, numerical simulations are carried out in MATLAB within an experimental environment. Results under standard test functions indicate strong algorithm stability and convergence, supporting the credibility of the optimization outcomes. Performance simulations further show that the resulting strategy achieves an optimal balance between dissemination efficiency and advertising expenditure, yielding a dissemination efficiency of 3,984 and an advertising cost of 9,783 yuan, thereby improving overall returns. These findings demonstrate the practical usefulness of the multi-objective locust optimization algorithm for guiding intelligent AIGC advertising placement.