This paper investigates the optimization problem of a fleet of electric vehicles (EVs) serving a set of time-specified customers, where the operator needs to optimize routing and charging problem jointly for each EV. In particular, regarding to the spatio-temporal varying electricity price, we consider incentive-aware customers and propose that the operator offers monetary incentives to exchange time flexibility of customers. In this manner, a win-win situation is achievable since time flexibility enables the fleet operator to obtain a routing and charging schedule with lower cost, whilst the customers receives monetary compensation. Specifically, we first devise a bi-level model whereby the fleet operator optimizes the routing and charging schedule jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. At the same time, the customers choose the optimal time flexibility by minimizing its own cost. Second, we tackle the complexity resulting from the bi-level and nonlinear problem with an equivalent transformation method. Eventually, we reformulate the problem as a single-level optimization problem, which later is solved by proposed Benders dual decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of our framework and proposed Benders dual decomposition algorithm, we carry out extensive numerical experiments using VRP-REP data from Belgium.