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Differential evolution (DE), a population-based multi-objective evolutionary algorithm, steers optimization search through swarm intelligence produced by co-operation and competition among individuals within the swarm. With its unique memory ability, DE can track the dynamics of the current search, adjust its search strategy accordingly, and achieve good global convergence and robustness without resorting to any information characteristic of the problem in question. DE proves exceptionally useful in solving complex optimization problems which cannot be solved by conventional mathematical programming methods. This paper applies DE to the multi-objective optimal allocation of water resources, treats the optimal allocation of water resources as a simulated biological evolution process, and conducts an optimal computation in a case study, which shows that the result of DE is both reasonable and efficient.