Abstract: This paper describes a method of multi-objective quantum-behaved particle swarm optimization (MOQPSO) based on quantum evolutionary mechanism for multi-objective operation of cascade hydropower stations. On the basis of the quantum-behaved particle swarm optimization (QPSO), this new method adopts external archive collection to store non-dominated particles and implements maintenance and dynamic update of the collection using individual dominance relations. It uses individual leadership to choose the previous best position of the whole particle population and the previous best position of each particle so that diversity in individual evolution directions can be maintained during the search. A chaos mutation operator can be added to the method to further enhance its local search capability and global convergence performance. The method has been applied in the operation of hydropower stations in the Wu River basin. The results indicate that MOQPSO could generate a Pareto solution set that combines the considerations of reliability and benefits and thus it would lay a theoretical basis for decision making.