High Utility Itemset Mining (HUIM) identifies itemsets based on utility, such as profit or significance, making it valuable in retail, healthcare, and finance. Conventional methods like Two-Phase and UP-Growth struggle with exponential search space, repeated database scans, and high memory use, limiting performance on large datasets. This paper introduces a hybrid metaheuristic combining Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA), enhanced with Bit-Vector Optimization (BVO) for efficient itemset representation. GA enables exploration, PSO accelerates convergence, CSA maintains diversity, while BVO reduces computation cost. Tests on IBM synthetic datasets and the Retail-FIMI benchmark show the proposed GA–PSO–CSA with BVO outperforms traditional and standalone metaheuristics in convergence speed, execution time, and solution quality, proving its scalability and effectiveness for real-world HUIM tasks.