In this research, we study a cost minimization problem for a firm that acquires capacity from providers to accomplish daily operations on telecommunication networks. We model the related optimization problem considering quality of service and capacity requirements and offer a solution approach based on genetic algorithm (GA). Our model reckons the tradeoff between the network capacity acquisition cost and opportunity cost arise when data transmission quality for real-time tasks manifested at undesired levels. To better represent the related features and complexities, we model both capacity and loss probability requirements explicitly, and then, formulate delay and jitter requirements as level matching constraints. Using an experimental framework, we analyze how optimal behavior of the firm is affected by different price schemes, transmission quality and task distributions. We also compare three GA based heuristic solution approaches and comment on the suitability of them on resource selection and task allocation problems.