Treatment process in health centers mostly operates in such a way thatthe patient should be hospitalized in several parts of the wards during the treatment. Transferring patients from one ward to another can be constrained due to the lack of vacant bed in next wards. As a consequence, the patients are stuck in their current wards. This phenomenon is called Blocking and it occurs when the capacity of the network queue is finite. In another word, the patients are not able to leave their wards after the completion of the treatment since the next ward is occupied by another patient. When the Blocking occurs the patients are obliged to stay in the department until a bed becomes available in the next department. Bed block ( Blocking ) leads to a dissatisfaction among the patients. Moreover, it escalates the overall hospital operational costs. Hospitals often suffer from lack of proper planning and ineffective management of the beds and the medical resources. Proper and meticulous planning helps to control the number of beds in each ward. Conversely, weak planning results in the blocking, cancellation of the surgery in the operating room, ambulance diversions and operational chaos. In this research, we tackle this issue by introducing a two-objective integer programming model to determine the optimal planning of the elective admissions and the number of required beds in each ward. Essentially, the model aims to reduce the system congestion. Model minimizes the hospital costs and the number of patients who are blocked. The model is solved using the -restrictions method. The final outputs from the model are the number of hospitalized elective patients, the appropriate number of beds in each hospital ward, the number of the bed-blocking and the number of the patients who are forced to discharge. Due to the complexity of the issue, a multi-objective simulation optimization model is provided which uses the metaheuristic algorithm. In this respect, five simulation optimization algorithms are in total implemented to solve the model and compare the results. By solving the presented approaches, Pareto optimal frontier is obtained in the various states. Furthermore, the optimized decision variables such as the number of the elective patients, the number of the beds in each hospital ward and the number of patients who are either blocked or forced to discharge are determined.