Infrastructure investment and management often incur high capital cost that dictates the decision making during the planning phase. Future operation and maintenance costs have been considered but in different methods. With the pressure of existing aging infrastructure, the need to cut down maintenance cost has been highlighted. Currently, there is a lack of a standardized framework for decision making. Delay in decision making will lead to construction delay and increase in construction cost. This MSc thesis discusses the various frameworks available and proposes the optimal framework. Life Cycle Cost Analysis (LCCA) provides a methodology to evaluate an object, e.g. a road, through including investment, operation, maintenance, dismantling and landfill or recycling. Higher cost saving will be possible to achieve if LCCA can be applied during the planning phase of the construction. Thus, LCCA is studied and applied on a case study of a section along a 9- km road in south of Sweden. By considering the specific site conditions such as soil profile and road alignment, this thesis evaluates its effect on LCCA. Sensitivity analysis is carried out on the cost output due to uncertainty variable input of the data. The aim of this thesis is to formulate a generic model/tool for decision making based on LCCA methodology on the project level. The rules and regulations provided by Trafikverket (Swedish Transport Administration) and ISO standard for life cycle costing are used as parameters during the analysis. The model described above can be used in different countries if these parameters are redefined by adapting local standards and conditions. This thesis demonstrates the application of the model with a case study to discuss the difference between two different alternatives. The case study compares two alternatives for the same stretch of the road. The first alternative proposes a longer bridge while the second alternative provides a shorter bridge. The result shows that the first alternative has slightly lower LCC compared to the second alternative. Through understanding all the costs connected to the project, cost calculation has been adjusted to ensure accuracy of the data input. Finally, the result from the cost comparison is tested with Monte Carlo Simulation to manage uncertainty risks.