Titanium (Ti) alloy ring groove rivet has been widely used in the aerospace industry due to its excellent riveting intensity and antilooseness. The grooving tool wear is more serious owing to the bad performance of Ti alloy machining and tool working environment is poor. Research is carried out on the machining process of Ti alloy ring groove as an object. The wear rate of rear face of grooving carbide tool was obtained in different cutting speed and feed rate parameters by using numerical method during cutting of Ti alloyring groove. The tool rear face wear rate prediction model was established based on the data analysis, and the parameters identification was carried out by the least square method. Genetic algorithm was applied to consider double objective optimization for tool rear face wear rate and machining efficiency, and the optimal combination process parameters was obtained. The results show the overall target value is increased by 2.9% after optimization of the process parameters.