<p>This study introduces an area partitioning methodology aimed at effective exploration of underwater search areas using cooperative multi-robot systems, specifically autonomous underwater vehicles (AUVs). In underwater search operations, target features are primarily detected using side-scan sonar (SSS), which provides acoustic images of the seabed using sound waves. When conducting exploration using SSS, maintaining a straight-line trajectory is crucial to acquire clear seabed images. Consequently, the direction and speed of ocean currents are critical factors in determining the directions of area partitioning and search patterns. Furthermore, the quality of acoustic images generated by sonar sensors varies depending on the seabed’s reflective properties, affecting sensing performance under different environmental conditions. To address these operational characteristics and the sensing capabilities of AUVs, we introduce the concept of time-adjusted workload (TAW) as the distribution of tasks among robots based on the estimated time required for each robot to complete its designated task. This approach emphasizes the importance of evenly distributing TAW among AUVs to minimize the overall mission completion time. Focusing on cooperative strategies and operational efficiency in marine environments, this research aims to enhance the effectiveness of underwater search missions through the optimized use of multi-AUV systems. The feasibility of the proposed methodology is demonstrated through numerical simulations of underwater exploration scenarios.</p>