To address the issue of position drift caused by water flow impact during bottom monitoring in high-flow areas by underwater monitoring robots, a method combining parametric modeling, fluid simulation, and multi-objective optimization algorithms was proposed for improving the external structure. Initially, a simplified parametric model of the robot was constructed, followed by a sensitivity analysis of the fluid domain conducted on the computational fluid dynamics platform. Optimal Latin hypercube sampling and a second-order response surface model were applied to fit the design variables and assess their sensitivity. Finally, a multi-objective optimization model was established based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), targeting minimal drag and minimal lift. After 20 generations of evolutionary iteration, the Pareto optimal solution set was obtained,and NSGA-Ⅱ algorithm was compared with other algorithms to verify its superiority in multi-objective optimization. The optimization results show a 6.3% reduction in drag, a 7.1% decrease in lift, and an 18.8% reduction in volume compared to the initial structure. Additionally, the corner rounding treatment eliminates stress concentration, and the design demonstrates superior stability under different flow velocities and turbulence intensities. The effectiveness of the optimization method is validated, providing theoretically and technically extensible support for the structural design of mission-oriented underwater robots.