Photosynthesis directly affects the quality and growth of blueberries, and the rate of crop photons is mainly influenced by temperature and photon yield density. At present, most greenhouse control did not consider the coordination of light temperature, and the actual energy consumption of greenhouses, not only resulting in meaningless waste of energy, but also creating a greenhouse small-climate environment which reduced the efficiency of blueberry photosynthesis. In order to solve the above problems, considering the photosynthemum and greenhouse cooling energy consumption during the spring and summer when blueberry was in flower fruit period, and the temperature and lighting control value of greenhouse were optimized by multi-target optimization algorithm. Firstly, the blueberry photosynthing rate model with temperature correction was established, which was based on the results of the temperature and photon pass density nesting test. Using a right-angled bi-curve correction model with temperature correction to model the blueberry photosynthetic rate. The model fitting results had a coefficient of determination (R2) of 0.9836, an average square root error of 0.5701μmol/(m2·s), and an average relative error of 3.86%, which can better reflect the relationship between blueberry photosynthing rate and temperature and light. Then a greenhouse energy consumption model was established, and the optimal solution of Pareto was solved by using NSGA-Ⅱ multi-objective optimization algorithm with greater net photosynthing rate and energy saving as the optimization goal. In order to further illustrate the optimization effect, different selection strategies were adopted for the optimization solution, which can reduce the energy consumption by about 21.3% while maintaining the photosynthetic rate of blueberries basically unchanged;under the premise of giving priority to planting benefits, the energy consumption can be reduced by 8.6% while the average increase of the photosynthetic rate by about 28.9%. The results can provide a theoretical basis for analyzing the physiological characteristics of crops and optimizing the greenhouse light temperature regulation setting. Greenhouse decision makers or control algorithms can use this method to set greenhouse temperature, light regulation settings. At the same time, the research method can also be applied to the setting value optimization of other crops which missing yield models in greenhouse production.