The performance of the bioretention system is significantly affected by plants and soil media, and its various diversified performance indexes are incompatible. However, the conventional evaluation methods are flawed by their strong subjectivity, which could easily lead to the errors of evaluation results. Here, the random forest (RF) model was used to screen the original data so as to reduce the dimensionality of the data. Then, the projection pursuit (PP) model was constructed to evaluate the hydraulic permeability and pollutant removal performance of different plants and soil media for multi-objective evaluation, and the model was solved by genetic algorithm (GA) and particle swarm optimization (PSO). The results of plant evaluation showed that Cyperus alternifolius L. was the optimal plant for the bioretention system, which was similar to the results of the analytic hierarchy process model and the back propagation (BP) neural network model. The evaluation results of soil media showed that RST2 (9.8% loamy sand+88.2% fine sand+2.0% vermiculite) was the optimal soil media configuration for the bioretention system, which was similar to the conclusion of the conventional projection pursuit method. These findings indicate that RF-PP model is suitable for multi-objective evaluation of the bioretention system.