Graphs are present in various fields of knowledge, representing a wide range of structures and systems, such as transportation networks, pathways, electrical circuits, among others. They are mathematical structures that model relationships between objects through vertices (points of interest) and edges, which represent possible paths between these vertices and may carry weights associated with distances, times, costs, or other relevant metrics. Graphs are widely used in the modeling and effective resolution of route optimization problems, aiming to find the best possible solution by minimizing or maximizing a specific metric. Although end users often do not interact directly with or need to understand their complexity, the benefits produced lead to positive outcomes in the user experience. In urban mobility, especially with electric vehicles, optimization enables the structuring of efficient routes, benefiting both users and the environment by reducing travel time, noise, and pollutant emissions. Recent applications include route planning for agricultural drones and autonomous vehicles, as well as the management of school and industrial transportation, consistently promoting sustainability, efficiency, and cost-effectiveness. Additionally, graphs are used in the Internet of Things, smart networks, and even in aerial systems, optimizing complex operations and enhancing safety and reliability. This paper presents a study aimed at developing a solution capable of addressing the needs of route optimization and providing effective responses to the challenges of urban mobility, considering the specific application context of electric motorbikes used in service delivery.