One of the basic capabilities of cognitive radio is to adapt the radio parameters according to the changing environment and user needs. A cognitive radio decision engine based on quantum genetic algorithm is proposed, in which the radio parameters are adapted and optimized by quantum genetic algorithm. The multiple objective functions are designed and multi-carrier system is used for performance analysis. Experimental results show that the proposed method has better convergence, precision and stability than the classic genetic algorithm, and the good performance of the proposed method in small population size illustrates that it is suitable for hardware implementation. Simulation results under different weighting scenarios illustrate the trade-off between multiple objective functions and that the adapted parameter configuration is consistent with the weights of the objective functions.