With the growth of the air traffic movements and the population of people living around airports, the number of awakenings due to aircraft noise has increased. ICAO has defined four research fields to reduce noise and one of them is implementation of noise abatement operational procedures. This includes different trajectory optimization methods, one of which is rerouting of trajectories around noise sensitive areas. Research has proven that rerouting has a positive effect on reducing the number of people getting disturbed while keeping the fuel consumption as low as possible. Until now these trajectory optimization problems focused on either a departure or an arrival trajectory. When implementing these optimized routes, it may result in a conflict with other existing routes and this is the problem that is the main focus of this research. Within this research trajectories will be combined and optimized for number of awakenings and fuel consumption while assessing the effect of terminal operations. The model that combines trajectories is based on existing models, including a point-mass model and a noise model. These are combined in a multi-objective evolutionary algorithmwhere the objectives are the number of awakenings and the fuel consumption. The results of the optimization problems are presented as Paretooptimal solutions. To contribute to the field of noise abatement terminal operations, two trajectories are combined in an optimization problem. To assure enough distance between the two trajectories the minimum separation constraint is used. When a loss of separation happens, the flight path angle of one of the trajectories is adjusted. The designed model is used to optimize four trajectory optimization problems. The first being a departure trajectory, this is the current Spijkerboor standard instrumental departure that departs from runway 24 at Amsterdam Airport Schiphol. The second problem is the current night standard terminal arrival route starting at sea and landing on runway 18R at Schiphol. For the third optimization problem the two trajectories of the previous optimization problems are combined with keeping the minimumseparation constraint in mind. The final case study was used to focus more on the effect of the minimum separation constraint. From the departure optimization problem can be concluded that there is a big diversity in the vertical and horizontal trajectory between the minimum fuel andminimumawakening solution. For the arrival optimization problem the difference between these two solutions is not significant. The vertical trajectory is for both solutions almost the same, the main difference is caused by the ground trajectory. When combining the trajectories the results of the objective functions for the combined problemare the sum of the arrival and departure objective functions. This results in a bias to the departure trajectory because these numbers are atleast twice the value compared to the arrival trajectory. This also results in the arrival trajectory always being created around the departure trajectory. From the final case study can be concluded that when designing the trajectories the influence of the minimum separation constraint is mostly on the vertical profile of the departure trajectory. Also can be observed that small rerouting of the trajectories sometimes is needed to give both trajectories enough space to cross each other.