In order to solve the shortage of local optimum in the process of solving the real time price model of smart grid by using traditional optimization algorithm, the Optics Inspired Optimization (OIO) is introduced to solve it. The variable fitness method is used to improve the constant fitness of the optics inspired optimization in the process of solving the model. The new algorithm is named Self-adaptive Optics Inspired Optimization (SAOIO) algorithm. According to the changes of the number of iterations, the adaptive degree can be changed by itself to improve the convergence speed and improve the accuracy of the solution. In the real-time pricing model of smart grid, the Lagrange dual algorithm and SAOIO are synchronously simulated. The results show that the SAOIO can better find the global optimal price, improve the accuracy, and have better practicability. This work is supported by National Natural Science Foundation of China (No. 71401106), and Humanities and Social Sciences of Ministry of Education Planning Fund (No. 16YJA630037), and Shanghai College Young Teachers Training Program (No. ZZsl15018) and Doctoral Scientific Research Foundation of USST (No. 1D-15-303-005).