<p>We propose using an adaptive sampling method to detect change points for a system with multiple lines. The adaptive sampling utilizes the information in responses to learn on which line is more likely to have a change thus allocating more units to the line. The learning process is formatted as a Markov decision process and the optimal sampling is approximated by using the Bellman operator iteratively based on the average reward criterion. We demonstrate the performance of the proposed method for binary responses using the exact distribution method for adaptive sampling. Our numeric results show that the adaptive sampling samples more often the line that has a change and the statistical power to detect a change is generally better than those with a bandit UCB method and the equal randomization except for a small sample size of 10 for the equal randomization. When sample sizes increase or the difference between out-of-control and in-control probabilities increases, the adaptive sampling allocates higher proportion of units averagely to the line with a change and the statistical power to detect a change increases. The statistical power under the bandit UCB is close to those with adaptive sampling when the sample size increases to 100 for a large difference between out-of control and in-control probabilities.</p>