In today's society, despite the fierce market competition for more income, it is necessary that the business models used are up-to-date and appropriate models. In many cases, over time, the business model no longer meets the needs, which managers realize that the business model needs to change. And the main factor causing the change must be found, which will incur huge costs for the business. Therefore, it is necessary to find the exact time of making changes so that the two main points of change can be followed by the factors causing the change. Therefore, the purpose of this research is to find the point of change in the business model and provide a new approach to study and examine the time of making changes in each business and take corrective measures to improve the performance of the model. In many businesses, the factors under investigation are both discrete and continuous, which is why this study examines these indicators simultaneously. In this paper, the maximum likelihood method is developed to estimate the change point in a business model with mixed continuous and discrete data. For this purpose, the inverse Norta method will be used to transform the data and the t-square control chart will be used to monitor the model. For this purpose, the type of change is considered to be a step type, and a real example related to a company active in the environmental field is used to demonstrate the capability of the presented model in estimating the change point. The simulation results show that the presented model has sufficient accuracy and precision to estimate the change point under different changes in the model.