This paper provides a comprehensive optimization strategy aiming at reducing the complexity of high efficiency video coding (HEVC) encoder with CPU-GPU cooperation. Based on the computational complexity distribution of HEVC encoder and characteristics of different modules and coding tools, intra coding, inter coding and in-loop filtering are collaboratively optimized. For intra coding, based on the correlation between neighboring coding units (CUs), depth range of CU is predicted and the number of candidates in intra mode candidate set for RDO (rate distortion optimization) is cut down, to avoid unnecessary computations. For inter coding, the most time consuming module, motion estimation (ME), is implemented with the collaboration of CPU and GPU in pipeline. Based on the energy of prediction residuals, an early termination scheme of CU splitting is proposed in this paper. For in-loop filtering, GPU based sample adaptive offset (SAO) parameter decision scheme and GPU based deblocking scheme are proposed to further reduce the coding complexity on CPU. The overall optimization scheme is implemented on the HM 16.2 platform, and experiments demonstrate that the proposed optimization scheme can reduce over 68% of the coding complexity of HEVC encoder, with only 0.5% performance loss in average.