s to solve the p roblem of port congestion , a self-learning genetic algorithm based on reinforce ment learning (GA- RL ) was proposed. In GA-RL, taking the genetic algorithm as the b asi c optimizat ion m odel , and Q-learn ing algo-rithm was use d to ada ptive ly adju st the c ros sove r and mutat ion par ameter s to improve the search ability of the algo rithm. At the same time, a dynamically adjustable Markov decision pr ocess  (M DP) model was  construct ed.  In the MD P model, in ord er to c omp rehens ivel y e valuate the popula tion perf orm- ance, a st ate se t based on the pop ulat ion fitness function was propo sed, a nd a reward me chanism for effe ctiv ely reducing the targ et va lue was desig ned. Fi nal ly, taking the comp rehens ive port area of H uanghua p ort as an exam ple, different group s of exam ples w ere selected for s imu latio n exper imen ts. The re- sults verif y the effect iveness of the m odel an d alg orit hm,  whi ch can si gnif icantly red uc e the waiting time of ships in port and improve the efficiency of port navigatio n.