The invention brings forward a constraining knowledge and elite individual strategy genetic algorithm fusion-based crop growth period model variety parameter optimization method and belongs to the field of crop model variety parameter optimization methods. The method comprises the following steps: data preparation is made for crop growth period model variety parameter optimization; an initial range of crop growth period model variety parameters is graded; a phonological period which has key constraints for the model variety parameters are determined, a measured data range of the phonological period having key constraints is estimated, an improved genetic algorithm is set to control initial parameter value, an initial species population is generated, a fitness function is built, gene direction factors of individuals are built, a selection operator is built via a roulette method, a direction guide crossover operator is built, a direction and elite individual guide mutation operator is built, and elite individual-based local search is conducted. The constraining knowledge and elite individual strategy genetic algorithm fusion-based crop growth period model variety parameter optimization method can help address problems of low efficiency, inconsistency between optimization results and variety biology, and the like; parameter optimization accuracy and parameter optimization efficiency are improved, and the method has scientific significance and practical value.