This study proposes a new multi-objectiveoptimism algorithm. The new algorithm combined thePareto Particle Swarm Optimization (PPSO) and GIStechnology and applied to the urban water managementissues successfully. The hybrid model integrates thePareto Neighborhood Crossover into the ParticleSwarm Optimization algorithm and through thecombination with the Geographic Information System(GIS). The optimization objective is to establish theconfiguration to achieve the minimization of costs andutilization to maximize water supply, while minimizingharm to the environment. PPSO towards high density ofpheromones achieve the boundary movement for thebest measures; enhance global search capability andimprove the convergence rate. Via solving themulti-objective model, the use of PPSO found theoptimal allocation of water resources in the programwith the help of a grid map. The results show that:compared with genetic algorithm and back propagationneural network, PPSO’s convergence performance caneffectively solve the problem of large-scale optimalallocation of water. Therefore, the multi-objectiveoptimization model not only possesses a stronger globalsearch capability and has high convergence speed andaccuracy in this paper.