Abstract A method for optimizing the combustion in an ultra-supercritical boiler is developed and evaluated in a 660 MWe ultra-supercritical coal fired power plant. In this method, Artificial Neural Networks (ANN) models are established for predicting the boiler operating and emission properties. To enhance the generalization of the ANN models, Computational Fluid Dynamics (CFD) simulation is performed to generate some data as training samples for ANN modeling, together with the historical operating data. The inputs of the ANN models are unit load, coal properties, excess air and air distribution scheme, and the outputs are thermal efficiency and NOx emission. Based on the ANN models, Genetic Algorithm (GA) is used to optimize the air distribution scheme to achieve a higher thermal efficiency and lower NOx emission simultaneously. The predictions of the thermal efficiency and NOx emissions show a good agreement with the plant data, with mean errors of 0.04% and 3.56 mg/Nm3, respectively. The results indicate that the use of CFD data can help generalize the ANN models. The application to a practical plant demonstrates that the proposed approach provides an effective tool for multi-objective optimization of pulverized-coal boiler performance with improved thermal efficiency and NOx emission control.