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The existing multi-objective wheel profile opti-mization methods mainly consist of three sub-modules:(1) wheel profile generation, (2) multi-body dynamics simu-lation, and (3) an optimization algorithm. For the first module, a comparably conservative rotary-scaling fine-tuning (RSFT) method, which introduces two design vari-ables and an empirical formula, is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability. For the second module, for the TRAXX locomotives serving on the Blankenburg–R(u)beland line, an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model (KSM). For the third module, a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization (PSO) is proposed to quickly and reliably implement the task of optimizing wheel pro-files. Finally, with the RSFT–KSM–PSO method, we pro-pose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–R(u)beland line, namely S1002-S and S1002-M. The S1002-S profile min-imizes the total wear number by 30％, while the S1002-M profile makes the wear distribution more uniform through aproper sacrifice of the tread wear number, and the total wear number is reduced by 21％. The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications.