The selection of process parameters plays an important role in determining the quality and performance of 3D-printed cementitious structures, as well as the overall efficiency of the construction process. However, the proper choice of these parameters varies depending on the application, materials, and geometric complexity involved. Furthermore, the diverse quantitative and qualitative metrics used to assess these parameters often complicate direct comparisons. This study introduces a systematic methodology for selecting process parameters using three multicriteria decision analysis methods (MCDM): TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), VIKOR, and AHP (Analytic Hierarchy Process). TOPSIS and VIKOR are used to rank parameter sets by their closeness to an ideal or compromise solution, while AHP derives criteria weights through pairwise comparisons. The three rankings are integrated with the Borda count technique to obtain a robust final ordering. An illustrative case study shows how the framework balances printability, dimensional accuracy, geometric complexity, and mechanical performance. The approach is generic and will be validated with experimental data in future work. The proposed methodology evaluates parameters such as printing speed, nozzle diameter, and environmental conditions like humidity and temperature to identify combinations that balance multiple performance criteria, including mechanical properties, printability, geometric complexity, and dimensional accuracy of printed elements. This framework offers a replicable approach for researchers seeking to improve the consistency and quality of 3D-printed structures based on cementitious materials by highlighting the most influential parameters for adjustment. The findings contribute to advancing additive manufacturing in construction by providing a structured strategy for process parameter selection, supporting the broader adoption of 3D printing technologies in the field.