The aim of this study is to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level is for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements either a multi-objective GA based on the ARIMA model or based on the dynamic regression model. The second level utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with the ARIMA model only. The results show the drawbacks of time series forecasting using the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In the second level, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.