A lattice-type yarn-dyed fabric pattern optimization method, comprising: classify lattice-type yarn-dyed fabric patterns according to components; establish a lattice-type yarn-dyed fabric pattern library according to the components; carry out gene encoding on these lattice-type yarn-dyed fabric pattern diagrams; select N lattice-type yarn-dyed fabric pattern diagrams from the lattice-type yarn-dyed fabric pattern library at random; carry out human-computer interactive evaluation on the N lattice-type yarn-dyed fabric pattern diagrams as initial populations, and select the next generation of population individuals according to the evaluation result and a roulette selection strategy; carry out gene variation on gene codes of the next generation of population individuals to obtain new population individuals; these new population individuals and the lattice-type yarn-dyed fabric pattern diagrams selected from the library at random form N new lattice-type yarn-dyed fabric pattern diagrams, repeatedly execute the evaluation and variation operations until the evaluation result of each lattice-type yarn-dyed fabric pattern diagram subjected to human-computer interactive evaluation meets the set conditions. By means of the optimization method, the requirements and preferences of target customers are accurately obtained, so that targeted design is carried out on the yarn-dyed fabric patterns and the blindness is reduced.