Commercial banks play an important role in the supply of capital in the financial market. Interest income of enterprise loans is one of the major sources of their profits. An enterprise or a debtor cannot repay debts due to the business failures will result in the increasing of banks' non-performing loans and lower their profits. Therefore, the assessment of enterprise credit risks, by analyzing a solvency of enterprises, becomes an important task. Better quality of enterprise credit rating can protect bank profits by reducing non-performing loan ratio and the total amount of overdue loans of banks. As business competitions among various financial institutions, they loosen the loan credit rating criteria in spite of the increasing of non-performing loans. In order to prevent the situation, Bank for International Settlements adopts more stringent assessment criteria to standardize a set of agreements "Basel Capital Accord" for the credit risk. Every financial institution therefore develops its own enterprise credit rating assessment criteria based on "Basel Capital Accord." Traditionally, a bank assesses a debtor’s loan credit rating according to subjective experience such as 5P principles or 6C principles. In recent years, as the advance in information technology, data mining technology is also more widely used in credit risk assessment. Many credit rating models are developed such as support vector machine model, Logistic model, neural network model, fuzzy radial basis function network model, etc. Different models have their own pros and cons. Our model applies the genetic algorithm to enhance the evaluation of loan credit rating of enterprises. Through the simulation of DNA encoding and propagation principles, better financial index assessment parameter weights are generated. These weights are then used to compare with existing enterprise cases and analyze the ability of their debt payments. Values of these weights are improved by running the process over and over until no better values are generated. These weights are employed to produce a value which is able to predict probabilities of bad debts of enterprises. Banks can then make better decisions depending on the output value of our model.