This paper introduces a method of using indicial functions （IFs） to simulate the time-domain expressions of self-excited aerodynamic loads of bridge decks， and studies the precision of this simulation. A modern genetic optimization algorithm is pro? posed to identify the parameters of IFs based on the tested flutter derivatives. During the simulation process， the equivalent relation between flutter derivatives and IFs parameters is first established. Then， the genetic optimization algorithm is implemented to iden? tify all the IFs parameters using the MATLAB software. Based on the obtained IFs parameters， the fitted flutter derivatives are calculated according to the relation expression between IFs parameters and flutter derivatives. Finally， the simulation precision is evaluated by comparing the fitted and tested flutter derivatives. Numerical results indicate that the genetic optimization algorithm has high computational efficiency and is not affected by the number or range of parameters. The number of IFs parameters greatly influences the fitting precision of the flutter derivative. When the number of IFs parameters is small， the fitting precision is not ideal for complex flutter derivative curves. As the number of IFs parameters increases， the fitting precision significantly improves. The difference in fitting precision directly affects the critical wind speed of flutter obtained by the subsequent time-domain flutter analy? sis. Therefore， it is necessary to carefully select the number of IFs parameters based on the properties of flutter derivative curves. This allows for the simulation of a high-precision time-domain self-excited aerodynamic loads model， which can accurately evaluate the flutter stability of long-span bridges.