In this paper, a multi-objective rule discovery approach is introduced to address the problem related to fatigue data editing. A set of rules are introduced to simplify the editing process, with which users can easily predict an appropriate level of damage for fatigue data segments subject to SAE1045 steel without need for a cycle-counting algorithm or stress analysis. A multi-objective approach called the elitist non-dominated sorting in genetic algorithm (NSGA-II) is modified and applied to find high-level knowledge representation IF-THEN rules in the eight-dimensional search space. Three possible outcomes represented with the labels very low, low and high are chosen for the rule consequent. Two conflicting classification objectives, the predictive accuracy and comprehensibility, are considered as the rule optimisation criteria. To accelerate the searching process, a sequential covering algorithm is applied in advance of the training session, which positively reduces data size and provides several simple rules. A set of seven high-accuracy and high-interpretability rules is discovered, and their uncomplicated implementation provides an alternative to the task of estimating the fatigue damage value and could be a part of a fatigue data editing process.