Every process in construction is strictly connected with costs and deadlines, which have to be met by the investor/owner and the construction company. More construction costs are covered by the construction equipment in road project. So, the Proper usage of equipment gives fast and accurate results at a reduced cost in a given project. Currently, idle cost and time of projects is a huge challenge in the Ethiopian construction industry because of improper selection of equipment or improper machinery combination. In most cases, selection and combinations are done based on experience but optimized by different analyses. Today, in Ethiopia, most road projects are performed using heavy equipment. However, an effective process is required to analyze the conditions carefully and choose the optimal type, number, and combination of equipment. This study uses queuing theory and dynamic programming methods to optimize idle time and associated cost of equipment to determine the optimal fleet of equipment to improve performance. The method was applied to two case studies by analyzing equipment productivity on sequence performance of loader & dump truck, dump truck & grader, and grader & roller based on the operation data. Data were collected using interviews and observation. However, based on the data, the idle cost and time of two combination equipment were evaluated using a queueing model based on the operation to simulate the operation rate (server capacity and the number of customers per hour) and finally optimize using the dynamic programming decision-making process to select the final equipment fleet. The outputs of the model are compared against the actual equipment fleet data to evaluate the validity of the queuing model and dynamic programming method developed. The result showed a reduction in idle cost by 15 % in case study one and 83 % in case study two. And also, the average performance of equipment utilization or output of equipment comparison of the loader, truck, grader, and roller increased in case study two by 34% but the first case study has the same. So the optimized fleet gives more productivity than the current fleet employed at the site. The case study one showed assigned of equipments combination that have similar capacity when to compared with case two. The relative low optimum values in case study one show their reach experience in the utilization or good assign combination of sequence the heavy equipment.