On the basis of the extended autoregressive model (ARX), a multi–objective function of the temperature, the humidity and the economic cost was established by introducing the artificial control factors. Multi–objective fuction optimization for environmental control was carried out in the tea–growing seedling greenhouse by using the grey correlation theory and the particle swarm optimization algorithm (PSO). The simulation results showed that the energy consumption could reduced by 17.6% under the control of the multi–objective PSO as the temperature dropped from 31.5 ℃ to 24.51 ℃and the humidity increased from 47.2% to 59.35%. Comparison with the linear weighted sum method and the single objective PSO, it could regulate the temperature and the humidity to meet the requirements for the growth of tea seedlings in greenhouse within 20 minutes under the condition of opening sun shading and spraying conditions.