With the enormous increase in energy usage by cloud data centers for handling various workflow applications, the energy-aware cloud workflow scheduling has become a hot issue. However, there is still a need and room for improvement in both the model for estimating workflow energy consumption and the algorithm for energy-aware cloud workflow scheduling. To fill these gaps, a new model for estimating the energy consumption of the cloud workflow execution and a novel Two-Stage Estimation of Distribution Algorithm with heuristics (TSEDA) for energy-aware cloud workflow scheduling are proposed based on the relationships among scheduling scheme, host load and power. In particular, in the proposed TSEDA, a new probability model and its updating mechanism are presented, and a two-stage coevolution strategy with some novel heuristic methods for individual generation, decoding and improvement is designed. Extensive experiments are conducted on workflow applications with various sizes and types, and the results show that the proposed TSEDA outperforms conventional algorithms.