In the context of climate change, wildfires are becoming more frequent and intense worldwide, expanding risks to previously unaffected northern regions. One of the most effective strategies for wildfire prevention and early detection is surveillance and intervention patrols. In France, these patrols are primarily conducted by the National Forest Organization relying on daily weather conditions and expert judgment to determine patrol routes. Our study focuses on designing a new approach to optimize surveillance patrols while taking into account real-world operational constraints. Surveillance and intervention patrol can be formulated as a team orienteering problem with time windows and time-dependent scores. In this work, a case study is conducted using real-world data in a department in the South of France. In which we model the daily patrols as the team orienteering problem with time windows and time-dependent profit, where a fleet of vehicles must cover high-risk areas based on tourist activities, fire risk estimations and weather conditions. Patrol routes are restricted by the duration of the daily shift, therefore not all locations can be visited and the focus must be on the riskiest ones. Each location should be visited during a specific time window, within which the periods carry a different score based on the fire risk levels variations throughout the day. Each point type (e.g. prevention or extinguishment) requires a different intervention time. The objective is to maximize the daily total score of each route. A literature review is also carried out in this work.