The purpose of this study is to identify which scale short-form development method produces better findings in different factor structures. A simulation study was designed based on this purpose. Three different factor structures and three simulation conditions were selected. As the findings of this simulation study, the model-data fit and reliability coefficients were reported for each factor structure in each simulation condition. All analyses were conducted under the R environment. According to the findings of this study, the increase in the level of misspecification and the decrease in the sample size can significantly affect the model-data fit. In a situation where the factor structure of the scale is getting more and more complex, model-data fit and Omega coefficients decrease. For scales with a unidimensional factor structure, all of the scale short-form development methods are recommended. For scales with multidimensional factor structure, Ant Colony Optimization, and Stepwise Confirmatory Factor Analysis algorithms and for scales with bifactor factor structure, the ACO algorithm is recommended. When viewed from the framework of metaheuristic algorithms, it has been identified that ACO produces better findings than Tabu Search.