The construction industry always strived for a successful project that is able to achieve its goals within the designated cost and time. Earned Value Management (EVM) is the common method to control project performance by forecasting the cost and time to completion (ECAC & ESAC). Previous studies have been conducted to calculate ECAC and ESAC using machine learning. However, due to the lack of optimization and the single-objective limitation, an improvement and development is needed. Using the optimizing model of Optical Microscope Algorithm (OMA) and applying bidirectional method, creates a new prediction model of OMA-NN-BiLSTM. Because of its conflicting nature and the interdependencies towards each other, cost and time are considered a tradeoff problem. A multi-objective algorithm, MOOMA, is developed to create a Pareto Curve that display the cost and time tradeoff values. Indifference Curve (IC) can be determine using triangular preference function to represents the decision-maker’s preference. The tradeoff result can be analyzed to calculate ECAC and ESAC based on the tangent point between Pareto Curve and Indifference Curve. Based on the selected project case period, the optimal tradeoff indicates that at the 4th period of the project, it is forecasted to be completed (ESAC) in 572 days with an estimated cost (ECAC) of approximately $2,332,958.70.

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