The Electric Vehicle Routing Problem (EVRP) has gained increasing attention with the growing adoption of electric vehicles, driven by the global shift towards reducing the negative environmental impact. This paper addresses the EVRP with Time Windows (EVRPTW), incorporating nonlinear charging functions that are often overlooked in existing models. Traditional EVRP approaches typically assume linear charging functions, which can lead to imprecise solutions due to unrealistic charging time estimations. However, real-world charging behaviour is nonlinear, and since recharging significantly affects total travel time, it is essential to account for more realistic charging dynamics. The objective of this paper is to solve the EVRPTW while minimizing both the total number of vehicles and the overall delay, using a more accurate representation of the charging process. To further assess adaptability, several recharging policies are examined, including minimal recharging, fixed-level recharging, and an adaptive policy that adjusts based on vehicle load. The proposed approach uses genetic programming as a hyper-heuristic to evolve routing policies that construct solutions while accounting for nonlinear charging behaviour. This work combines automatically evolved routing policies via genetic programming with both nonlinear charging models and different partial recharging strategies, providing a more realistic EVRPTW formulation.