A new method, node ordinal encoded genetic algorithm (NOEGA), is proposed for solving water resources optimal allocation problems, in which the capacity of water resources is split into a number of smaller parts so that successive operations can be overlapped. Our objective is to maximize the whole benefit function. To overcome the “dimensionality and algorithm complexity curse” while searching for solutions and looking for an optimal solution, the operations of one-point crossover operator, gene exchange operator, gene random operator, gene shift operator and node ordinal strings are established. It is proved to be an effective optimal method in searching for global solutions. The NOEGA does not need a diversity of initial population, and it does not have the problem of immature convergence. The results of two cases show that using NOEGA to solve the optimal allocation model is very efficient and robust. In addition, the algorithm complexity of NOEGA is discussed. A new method, node ordinal encoded genetic algorithm (NOEGA), is proposed for solving water resources optimal allocation problems, in which the capacity of water resources is split into a number smaller part so that successive operations can be overlapped. Our Objective is to maximize the whole benefit function. To overcome the “dimensionality and algorithm complexity curse” while searching for solutions and looking for an optimal solution, the operations of one-point crossover operator, gene exchange operator, gene random operator, gene shift operator and node ordinal It is proved to be an effective optimal method in searching for global solutions. The NOEGA does not need a diversity of initial population, and it does not have the problem of immature convergence. The results of two cases show that using NOEGA To solve the optimal allocation model is very efficient and robust. In addition, the algorithm complexity of NOEGA is discussed.