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In order to improve the hydraulic and structural performances of the double-channel pump, the uniform test design, fluid-structure interaction calculation, combination of artificial neural network and multi-objective genetic optimization method were applied. The inlet diameter, inlet width, tongue angle and diffusion length were selected as optimization variables, which were then used to produce 50 tests by using the method of uniform test design. The efficiency and maximum stress, attained through fluid-structure interaction in a volute at the design condition, were chosen as the optimization goals and then were applied to get the approximate function by BP neural network training. Special multi-objective genetic optimization strategy was designed to solve the function and search the optimized results which are called the Pareto frontiers of geometry parameters of the volute. During the process of searching the Pareto frontiers, the efficiencies, heads and stresses were chosen as constrains to get the final two optimizations. The results showed that compared with the original case, pressure diffusion effects of the optimized cases were improved obviously, backflow phenomena was reduced, and the efficiencies of cases opt1 and opt2 were increased, while the maximum stresses and the mean vibration velocities of opt1 and opt2 were depressed. The results indicated that the proposed multi-objective and multidisciplinary optimization method can obviously improve the hydraulic and structural performances of double channel pump, meanwhile, it has some reference value for optimization researches on other turbomachineries.