The adaptive implementation of the Monte Carlo Method (AMCM) has been applied to optimize the calibration uncertainty of a positive displacement meter against a standard tank in an ISO 17025 accredited volume laboratory. These meters are used for custody transfer in liquid hydrocarbon logistics where any reduction in the uncertainty estimation may have an important economic impact. Several innovations are proposed when applying AMCM; regulation of AMCM convergence by applying the Student's t-distribution, validation of repeatability and filtering of outliers by performing 50 iterations of the AMCM, and characterization of the measurand's Probability Density Function (PDF) which results in a Flatten Gaussian. This work proves that GUM method does not assign the correct PDF. The uncertainty estimation is reduced 7.1 % compared to GUM method.