Reliability, Availability and Maintainability (RAM) are used to increase and/or maintain operational availability in both industries where failures result in catastrophic damage and scrap output. Even if increasing/maintaining high availability avoids/reduces failure effects by executing maintenance before a failure occurs, maintenance may not be cost-effective in businesses where the consequence of failure is scrap output. The study's goal was to incorporate RAM into maintenance cost analysis in order to avoid those uneconomical expenditures in the case of the Amhara Pipe Factory (APF). RAM was combined with maintenance cost analysis in two stages: (1) optimizing unavailability and cost; and (2) maintaining optimal availability by enhancing reliability and/or maintainability based on the importance measure's value. These were done, once the time between failures (TBF), time to repair (TTR) and scrap in each failure of each component as well as associated costs were collected. Statistically, distributions were fitted by selecting best using Anderson Darling (A-D) goodness of fit test and correlation coefficient. The parameters‟ values were determined by maximum likelihood estimation (MLE) and least square estimation (LSE) methods in Minitab software. The optimization was carried out in MATLAB software using the non-dominated sorting genetic algorithm-II (NSGA-II), which outperforms other tools in terms of convergence to true optimal and diversified solutions for both objectives. NSGA-II programming provided fifty Pareto optimal solutions among them availability 56.96% was chosen based on sensitivity index value. The maintenance was planned based on the optimal availability obtained or availability requirement, with the least available component being considered first because the components have a series functional link to the line's intended operation. By reducing and avoiding most work in process scrap as well as unnecessary maintenances, the maintenance plan devised can save birr of 1,354,662 in two years. The study showed how RAM integrated to maintenance cost. The implementation model developed helps how other companies integrate RAM into maintenance cost and the factory use it, using Bayesian Inference estimation, in addition to MLE and LSE to prepare preventive maintenance plan. Key words: RAM, NDSG_II, independent and identical distribution, maintenance cost.