Maximum Entropy (MaxEnt) model has been widely used in recent years. However, MaxEnt is highly inclined to produce misleading results if it is not well optimized. We summarized the researches about the model optimization for sampling bias correction, model complexity tuning, presence-absence threshold selection, and model evaluation. Spatial filtering performs best for sampling bias correction, while restricted background method shows the lowest efficacy. Model complexi-ty is mainly determined by three factors: The number of environmental variables, model feature types, and regularization multiplier. Variables filtering is needed when sample size is less than the number of environment variables. The criterion of variables selection should focus on their ecological significance rather than the co-linearity between them. The choice of feature types has relatively limi-ted effects on predictive performance of the model, therefore it is advised to choose simpler models. To control overfitting, it is necessary to conduct species-specific tuning on regularization multiplier, which was usually bigger than the default setting. There are three criteria called objectivity, equality and discriminability for selecting threshold to convert continuous predication (e.g. probability of presence) into binary results. Maximizing the sum of sensitivity and specificity is a sound method for threshold selection. Model evaluation methods could be classified into two main types: Threshold-independent and threshold-dependent. Among the threshold-independent evaluations, information criteria may offer significant advantages over AUC and COR. True Skill Statistics is a better index for threshold-dependent evaluations, because it takes both omission and commission errors into account, and is robust to pseudo-absence assumption and species prevalence.