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To effectively and practically solve the dynamic economic optimal control problem of greenhouse environment with mixed integer variables, an improved genetic algorithm (IGA） with engineering constraint rules was proposed. Based on piecewise constant method for discretizing the control variables, the optimal control problem was transformed into nonlinear programming (NLP） problem with finite-dimension parameters, and the standard genetic algorithm (SGA） was used to solve the NLP problem. A precise penalty function was used to deal with the state variable path constraints. The engineering constraint rules and some improvement measures of elite retention, multi-population parallel evolution and integer variable setting were used to improve the algorithm performance. The simulation results show that compared with SGA, IGA obtains better performance indexes and control quality, which proves the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]