Recently, researchers adopted object-oriented method to extract wetland distributions. Multi-temporal and multi-sources of data can facilitate the extraction process but meanwhile it enlarges the amount of features. It needs a large quantity of experiment based on the expert knowledge to determine the optimal feature sets and the threshold values. In order to improve the classification accuracy and relief the researchers from large amount of work, a filter-wrapper hybrid feature selection method combining relief F, multi-objective genetic algorithm and random forest was proposed, which was a two-step method. In the first step, relief F algorithm was adopted to select features with class separability. In the second step, multi-objective genetic algorithm based on random forest (MOGARF) was built. Four measures such as out-of-bag (OOB) error of random forest algorithm, dimension of the feature space, correlations among features and the variable weight of relief F algorithm were acted as four objectives of MOGA. The probability whether the feature was expressed was determined by the variable importance measures from random forest algorithm. The crowded distance of each feature collection was calculated and the feature collection with the least crowded distance was the optimal feature set. Nanweng river basin was taken as the study site. Object-oriented classification using random forest classifier was conducted based on the optimal feature set. Then the result was compared with three other random forest classification schemes by using the entire feature set or the feature set selected by relief F algorithm or the Boruta algorithm. The classification scheme with MOGARF had the best performance and the feature dimension was reduced to 10% of the entire one. The overall accuracy reached 92.61% which was 0.35%~1.94% higher than those of the other three schemes with Kappa coefficient of 0.9306. The OOB error of MOGARF was 7.77% which was 0.91%~1.48% lower than those of the other schemes. All these indicated that the MOGARF feature selection method was an effective feature selection method when it was combined with random forest classifier.