Conditional nonlinear optimal perturbation (CNOP) is an extension of linear singular vector(LSV) to nonlinear optimization. Generally, CNOP is solved with such adjoint based algorithms as SPG2, SQP. Unfortunately, it is often difficult to obtain the corresponding adjoint models for some nonlinear models. In addition, for nonlinear models containing discontinuous “on-off” switches, the adjoint based methods can hardly find the correct gradient direction for solving the CNOP. These two factors restrict the application of CNOP. Intelligence algorithms are utilized to handle these problems and has made some improvements. Nevertheless, the intelligent method cannot be applied to solve CNOP of complex models due to its dimensional limitation. Therefore, the principal component analysis based genetic algorithm (PCAGA) is proposed to solve the CNOP of complex models. The PCAGA is composed of two key processes: dimension reduction and genetic optimization. To demonstrate the validity, PCAGA is applied to solve the CNOP of the Zebiak-Cane (ZC) model for studying ENSO predictability and compared with the adjoint based method. Experimental results show that PCAGA can achieve similar results to the adjoint based method in the high-dimensional space of a medium- complexity model without the adjoint models. The proposed method also can future be applied to solve variational data assimilation (VDA).