Abstract: In order to reduce the switching loss of DC-DC converter at the output port of bi-directional electronic transformer, a capacitor-inductor-inductor-capacitor（CLLC）resonant circuit is widely used. One of the problems is the selection of resonance parameters, which will directly affect the output performance of electronic transformer. A quantum particle swarm optimization（QPSO） algorithm is introduced to implement the intelligent optimization design of resonance parameters. The algorithm can optimize the objective function with non-differentiable points, and the quantity wave equation is used to enhance the particle search ability and to solve the local convergence problem of particle swarm optimization（PSO） algorithm. In the optimization process,a resonance point partition method is applied to establish the mathematical model of DC-DC converter, and the relationship between resonance parameters and the characteristics of the resonance network including the DC gain, input impedance angle and relative loss rate are analyzed to obtain the constraints of parameter optimization. On this basis, QPSO is used to obtain the resonance network parameters of the relative minimum loss of the circuit. Finally, taking CLLC resonance converter as an example, it is verified by experiments that in the wide-input-voltage and full-load-range conditions, the efficiency of the optimized design method based on QPSO algorithm is about 1% higher than that based on the traditional fundamental analysis（FHA） method and time domain method, and the highest efficiency of prototype reaches 97%. Thus the effectiveness of the optimization method proposed is proven.