Significant temporal variability exists in the charging demand of ride-hailing vehicles， but the traditional static location methods tend to cause problems such as insufficient coverage during peak peirod and resource waste during off-peak period. To address these issues， a location optimization model for ride-hailing vehicle charging stations that considers temporal variability was proposed. First， an in-depth analysis of the temporal distribution of charging demand was conducted based on Nanjing's ride-hailing vehicle operation data. The whole day was divided into three periods： peak， shoulder and off-peak periods. Differentiated weights and service time thresholds were set according to the proportions of demand and user sensitivity. Second， a dual-objective model was constructed with the goals of "minimizing total deadhead time cost" and “minimizing uncovered penalty in high-difficulty areas”， and the difficulty of ride-hailing was introduced as a priority adjustment factor. Constraints such as station service capacity and regional balance were established. Finally， by integrating the local fine exploitation ability of Artificial Bee Colony （ABC） and the global exploration ability of Grey Wolf Optimizer （GWO）， an Improved artificial Bee colony-grey Wolf hybrid Optimization algorithm （IBWO） was proposed to solve the proposed model， and adaptive parameter adjustment and population restart mechanismes were also added to avoid the local optimum. Using the core area of Nanjing as a case study and optimally selecting 100 stations from 1 465 candidate locations， the proposed model achieved 100% demand coverage across all periods， with uncovered demand in high-difficulty areas at 0. After 100 iterations， compared with the optimal results of Genetic Algorithm （GA）， Particle Swarm Optimization （PSO）， GWO and ABC for solving the proposed model， the optimal fitness value of IBWO is 16 180.1， which is 35.1% lower than that of the best-performing ABC （24 929.8）； the number of stations of IBWO is reduced by 50.7% compared with ABC （203）； the calculation time of IBWO is 334.87 s， which is 14.5% shorter than that of ABC （391.75 s）. The proposed model can accurately adapt to temporal fluctuations in charging demand， and the proposed IBWO offers fast calculation speed and avoids local optimum. This paper establishes a quantitative correlation framework between charging demand temporal dynamics and location decision-making， improves the infrastructure location model under multi-period constraints. They can provide accurate decision-making basis for the planning of urban electric ride-hailing vehicle charging facilities， reduce construction costs by more than 50% while ensuring all-weather service quality， and contribute to the efficient coordination of transportation and energy systems under “carbon peaking and carbon neutrality” goal.