This study tackles the gate allocation problem (GAP) at the airport terminal, considering the current covid-19 pandemic restrictions. The GAP has been extensively studied by the research community in the last decades, as it represents a critical factor that determines an airport's capacity. Currently, the airport passenger terminal operations have been redesigned to be aligned and respect the covid-19 regulation worldwide. This provides operators with new challenges on how to handle the passengers inside the terminal. The purpose of this study is to come up with an efficient gate allocator that considers potential issues derived by the current pandemic, i.e., avoid overcrowded areas. A sim-opt approach has been developed where an evolutionary algorithm (EA) is used in combination with a dynamic passenger flow simulation model to find a feasible solution. The EA aims to find a (sub)optimal solution for the GAP, while the simulation model evaluates its efficiency and feasibility in a real-life scenario. To evaluate the potential of the Opt-Sim approach, it has been applied to a real airport case study.