In order to solve the problems of insufficient model coupling， lack of dynamic response mechanism and imperfect consideration of multi-pollutant collaborative emission reduction in the co-optimization of airspace efficiency and environmental benefits in the existing research on trajectory optimization， this paper proposes a dynamic trajectory adjustment method based on airspace complexity and collaborative optimization of pollutant emissions. Firstly， a spatio-temporal interaction-driven air traffic complexity calculation model is constructed. Secondly， the system for assessing aviation’s global emissions （SAGE） and modified index models are integrated to establish a multi-pollutant emission calculation system during the cruising phase. Finally， an improved simulated annealing genetic algorithm （SAGA） is designed to solve the multi-objective optimization problem. Simulation experiments based on navigation data on a certain day in June 2023 show that under the ideal situation of not considering airspace restrictions and other constraints， by adjusting 26.18% of the flight altitude， the proposed method reduces emissions of NOX， CO， CO2， SO2， HC by 0.75%， 0.14%， 0.47%， 0.48%， 0.33%， respectively， and the average complexity of each time period is reduced by 19.82% on average. The overall average complexity of the whole day is also reduced. This method realizes multi-objective collaboration through dynamic adjustment of the height layer， which significantly improves the environmental benefits while ensuring operational safety， and provides technical support for the green transformation of the air traffic control system.