Completion of a project on time is crucial for its stakeholders when the competitive environment in all industries is considered. This favorable target is achieved by finding the optimal set of time-cost alternatives, which is known as time-cost trade-off problem (TCTP) in the literature. In this study, a new initial population approach is presented to improve the quality of the optimal set of time-cost alternatives. It employs a predefined number of solutions to the single objective TCTP into the initial population of teaching learning-based algorithm, which is an optimizer for the multi-objective optimization of TCTP. Hence, it is aimed at descending randomness on the initial population and decreasing searching effort to catch the optimal set of time-cost alternatives in the search space. The proposed methodology is tested on a series of benchmark problems and the solutions obtained are compared with those available in the technical literature. Results show that the present method can produce favorable solutions as effective as other techniques applied for simultaneous optimization of TCTPs.