In order to match the test bending moment with the target bending moment in the fatigue test of wind turbine blade， an intelligent optimization scheme that uses an improved intelligent optimization algorithm for the arrangement of equivalent weights is proposed to accurately obtain the blade's fatigue characteristics. Through the identification of modal test parameters， the excitation frequency of the rotating mass equaling to the first-order natural frequency of the blade is determined， and a section bending moment calculation model is constructed to introduce the bending moment component of the blade's weight. Based on the hybrid particle swarm optimization algorithm introduced differential evolution mutation， the jointly optimization of bending moment distribution and amplitude control problems is performed using the mean square error as the fitness function. Using the LZ40.3-1.5 blade for optimization， it is concluded that the main influencing factors of the bending moment distribution of fatigue test are the number， quality and position of the vibration excitation device and counterweight. The bending moment's errors at the key section are controlled within 7% by the designed algorithm， verifying the correctness and feasibility of the counterweight optimization scheme of bending moment matching in the uniaxial fatigue test.