## 1 Introduction  
The increasing popularity of electric vehicles (EVs) due to environmental concerns is also increasing their use in the logistics sector day by day. With the tightening of environmental protection policies, regulations have been introduced to prevent the distribution of traditional fossil fuel vehicles in urban areas of some cities [1]. While EVs are rapidly being adopted to reduce global carbon emissions, the effects of the transformation in the transportation sector have begun to be seen and have become an important alternative to fossil fuel vehicles. This transformation is also prominent in last-mile delivery, which is all logistics activities related to delivery to customer households in urban areas. The growth of urban populations and intense e-commerce activities increase the complexity of last-mile delivery and its impact on the environment and quality of life. More and more logistics companies are adopting EVs for cargo distribution [2]. Despite the environmental benefits of EVs, their limitations, such as limited driving range and lack of charging infrastructure,continue. These problems also lead to difficulties in the route planning of EVs. Innovative solutions are needed to overcome these challenges and promote the widespread adoption of electric vehicles for last-mile delivery. These solutions should focus on improving battery technology to extend driving ranges [3], expanding charging infrastructure in urban areas [4], and implementing efficient vehicle scheduling and route planning algorithms that maximize delivery efficiency while minimizing costs [5; 6; 7]. Therefore, it is crucial to establish effective vehicle routing and scheduling, including operational costs, that can not only reduce total travel time but also reduce delivery costs. Developing and implementing innovative solutions for last-mile delivery using electric vehicles is essential to address the environmental impact and logistics challenges associated with traditional fossil-fuel-based operations. Last-mile delivery has become an important need with the increasing quality of life in cities. In the increasingly competitive environment, the need for customer-satisfaction-oriented route optimization in fleet management of service providers is also obvious. Various technologies and strategies are proposed in the literature for task planning and route optimization of electric vehicles for last-mile delivery. Studies include the use of optimization algorithms, machine learning techniques, and real-time data analysis to optimize delivery routes based on factors such as traffic conditions, delivery time windows, and charging station availability [8]. By integrating these technologies and strategies, logistics companies can perform more sustainable and efficient last-mile delivery operations while reducing their carbon footprint. When the literature is examined, it is seen that studies on route optimization of electric vehicles in last-mile logistics focus on cost-oriented objectives. In the literature, it has been observed that studies that take into account the time window constraint work on cost-oriented objective functions such as minimizing total distance, total time, and total energy. It seems that there is a need for studies that take into account objective functions such as the extent to which routes meet customer constraints based on time windows, total tardiness, and the number of customer requests that cannot be met on time. These issues are of vital importance for businesses that prioritize customer satisfaction. The aim of this study is to address route optimization for customer-oriented objectives as well as cost-oriented objectives such as total travel distance and total travel time by considering charging time. The charging strategy applied in routing electric vehicles affects the total route duration, including the time the vehicle is at the charging station. In last-mile delivery, customer satisfaction is related to whether a certain product is delivered within the specified time interval.