This paper presents a novel approach to combine Monte Carlo optimization and nuclear data to produce an optimal adjusted nuclear data file. We first introduce the methodology, which is based on the so-called “Total Monte Carlo” and the TALYS system. As an original procedure, not only a single nuclear data file is produced for a given isotope but virtually an infinite number, defining probability distributions for each nuclear quantity. Then, each of these random nuclear data libraries is used in a series of benchmark calculations. With a goodness-of-fit estimator, a best evaluation for that benchmark set can be selected. To apply the proposed method, the neutron-induced reactions on 239Pu are chosen. More than 600 random files of 239Pu are presented, and each of them is tested with 120 criticality benchmarks. From this, the best performing random file is chosen and proposed as the optimum choice among the studied random set.