The continuous growth of air transportation demand and the tightness of airspace resources in the terminal area of a hub are becoming more and more prominent. In this paper, a Dynamic Programming Approach to Limit Optimization (DPALO) is proposed to solve the Arrived Landing Problem (ALP). Firstly, a discrete mathematical model of flight landing scheduling with time window constraints is established, and a recursive formula for solving ALP with fixed order is derived. The flight time window is optimized by combining the ALP problem characteristics with the constraints, and it is proved that the proposed method does not affect the solution of the optimal value of the model. Next, elite genetic algorithms, particle swarm algorithms, linear loop swapping and linear loop interpolation are applied to adjust the flight sequences with a view to finding an optimal solution. Finally validation is performed on OR-Library dataset. The experimental results show that using elite genetic algorithm to adjust the flight landing sequence, DPALO outperforms the known optimal solution (BKV), bionic algorithm (BA) and displacement decision algorithm (DALP). Similar to the results of the cellular automata optimization approach (CAO), the tight subsequence algorithm (CSA), and the hybrid particle swarm optimization-local search algorithm (RH-HPSO-LS), DPALO achieves milliseconds in time on the small sample dataset, and improves its time efficiency on the large sample dataset in comparison to CSA, CAO, and RH-HPSO-LS time efficiencies of 76.88%, 89.11%, and 78.28%, respectively.