Abstract Wildfires are a natural phenomenon that regularly occurs in many terrestrial ecosystems. Due to global warming, the rate and the span of wildfires have remarkably increased during the last years, causing important economic losses and human casualties. Several initiatives have been undertaken in the last years in order to apply operations research tools to help firefighting teams schedule and optimize their protection activities when dealing with wildfires. In this context, a recent variant of the Team Orienteering Problem, referred to as the Asset Protection Problem, was proposed in van der Merwe et al. (2015). In this problem, firefighting teams provide a protective service to a set of assets endangered by wildfires. These activities can be performed by a heterogeneous fleet of vehicles and occur within specific time intervals estimated on the basis of fire fronts progression. This problem incorporates three additional constraints: time windows , synchronized visits , and compatibility constraints between vehicles and assets. In this paper, we propose a hybrid approach that combines a Greedy Randomized Adaptive Search Procedure coupled with an Iterated Local Search (GRASP ILS) and a post‐optimization phase based on a set covering formulation. Interestingly, GRASP ILS incorporates an adaptive candidate list‐based insertion heuristic and a Variable Neighborhood Descent search procedure. Detailed computational tests were carried out on benchmark instances from the literature. The results show that our method outperforms the other methods in the literature, since it improves all the best‐known solutions on medium‐ and large‐size instances, while maintaining shorter computational times.