This paper presents a material requirements planning method that determines optimal safety stock levels using a heuristic optimization, based on a deterministic simulation of stock levels. Material requirements planning is a key competitiveness factor in a volatile, global market environment and is becoming increasingly complex due to the availability of more products, product variants and fluctuating demand. Digitalization offers significant potential benefits for this planning domain, however, tools ready for use in industry applications are still lacking, leading to untapped potential in companies. The approach presented herein investigates available safety stock calculation algorithms, develops a heuristic-based optimization method that determines the best fitting algorithm for each product and optimally parameterizes the algorithm. The method utilizes a deterministic simulation as an evaluation function. A case study for a company in the capital good industry is implemented to evaluate the application potential. The results reflect significantly improved service levels with a minor increase in cost.