Solving expensive optimization problems is often accompanied by computational cost disasters. To reduce the number of real evaluations of the objective function, this study uses the ordinal prediction method in the selection of candidate solutions in evolutionary algorithms. The relative quality of candidate solutions is directly obtained through classification prediction, which avoids the need to establish an accurate surrogate model for the objective function. In addition, a reduction method for the ordinal sample set is designed to reduce the redundancy of the ordinal sample set and improve the training efficiency of the ordinal prediction model. Next, the ordinal prediction is combined with the genetic algorithm. The simulation experiments of the ordinal prediction-assisted genetic algorithm on the expensive optimization test function show that the ordinal prediction method can effectively reduce the computational cost of solving expensive optimization problems.