Unlike gene knockout based simulation, in silico identifying gene overexpression targets has more uncertainties to be experimentally verified because of the difficulties for exactly manipulating fluxes to certain values. To overcome this challenge, methodologies have been developed for simulating gene overexpression, such as OptForce (Ranganathan et al., 2010) and FSEOF (Choi et al., 2010), as well as their derivatives (Chowdhury et al., 2014, Park et al., 2012). By using enforced flux and flux variability analysis, gene targets with desired up-regulation were successfully simulated and experimentally verified. However, those overexpressed gene targets were mostly identified to coordinate with additional manipulations (e.g. knockouts or down-regulation), whereby overexpressing some targets such as targets in glycolysis may not always independently contribute to an overproduction. Therefore, it is important to know the contribution of each candidate targets toward the theoretical maximum yield to fulfill the growing needs on customized pathway design. In addition, most current modeling methods still require specific programming skills that restricts the access for biologists and broad users. It is highly desirable to develop the software platform that can bridge the technical gap between computational modeling and bench works. In this paper, we present a software package, UP Finder that facilitates the identification of gene overexpression strategies for the metabolic engineering of targeted overproduction. It highlighted the quantitative evaluation for each overexpression candidate on yield contribution. The graphical user interface of the UP Finder also provided easier access for broad users. Two typical examples in metabolic engineering that lycopene precursor and fatty acyl-ACP overproduction were used to evaluate feasibilities of the UP Finder for analyzing biosynthesis pathways of natural products and biofuels. The identified gene targets by the UP Finder showed high degree of agreement with the reported key genes in the literatures.