Models presented in inventory management, encompass varied parameters. Primary factor in classic models related to determination of the economical ordering quantity (EOQ) and the economical production quantity (EPQ), is to consider parameters like the setup cost, the holding cost and the demand rate, to be fixed. This characteristic leads to a great difference among the quantity of the economical ordering obtained in classic models and real-word conditions. For instance, It should be stated that not only the holding costs of spoiled and useless products are not always fixed, but also, they would be increased by passing time. This article is an attempt to develop classical EOQ and EPQ models by considering holding and purchasing cost as an increasing continuous function of the ordering cycle time. Due to the complexity of the considered problem, two meta-heuristic algorithms, including Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO) are developed. Optimizing service level is considered as one of main apprehension in management science, that’s why increasing service level optimization would be evaluated as the second objective. As the performance of meta-heuristic algorithms is significantly influenced by calibrating their parameters, Taguchi methodology has been used to tune the parameters of the developed algorithms