With an annual passenger capacity of more than 4 billion and a rapidly increasing number, air transport is the undisputed leader among other travel options for long-distance traveling. There are unique airline connections between more than 20 thousand cities in the world. Despite this, many passengers fly via connecting as there are no direct flights from many cities to many cities or because direct flights are sometimes not economical or common. Since passengers do not like to wait in transfers, passengers are lost to other flights and airline companies if the airline does not have a suitable flight at the appropriate time for the transfer. Arrival and departure synchronization is vital at major hub airports and major airlines. In this study, the synchronization of arrival and departure flights is solved for Istanbul airport, an important hub airport worldwide, using data from a prominent airline company. Three metaheuristic methods, Evolutionary Strategies (ES), Modified Discrete Particle Swarm Optimization (MDPSO), and Random Search (RS) were used, and the results were compared with the original plan (OP). ES and MDPSO were found to be promising solution methods, and they provided substantially increased acquired passengers during connecting flights.