Recently in the construction industry, Project Portfolio Management (PPM) has become prevalent; it is expected that multiple projects under a single management umbrella will maximize benefits that will not be possible if the projects were managed independently. Although, while multiple projects run in parallel and demand the same resource (cash) simultaneously, which causes an inevitable cash shortage, but the surplus cash in a project will be used to schedule another project. Following this concept in scheduling, all existing and planned projects can be related to the overall liquidity situation of the contractor. So how to determine, manage and balance the right scheduling of projects is crucial to any contractor operating in a multi-project environment. Proper cash-flow management is necessary to ensure that a construction project finishes within time, on budget, and yields a satisfying profit. Poor financial management might put the contractor, in a situation where be unable to finance the project due to insufficient liquidity, or engaged in excessive loans to finance the project, decreasing the profit, and even creating unsettled debts. Engagement with a portfolio of large construction projects, like infrastructure projects, makes attention to finance more critical, due to large budgets and long duration. The objective of this paper is to present a Finance based Scheduling Optimization Model using Enhanced Genetic Algorithm (FSOMEGA) that aims for maximum expected profit, minimized financing cost, and minimized extension of work schedule beyond the contract duration at the portfolio level. The model is coded by Python programming language, and it generates an optimal / near optimal work schedule that leads to maximum profit and adjusting the start/finish times of projects' activities in case of a limited or unlimited budget. An illustrative example will demonstrate the features of this model and the model validation is put into consideration.