The integration of a substantial number of plug-in electric vehicles (PEVs) into power grid scheduling introduces complexities due to stochastic charging and discharging behaviors, which pose significant challenges to the stability and reliability of the power system. The PEV scheduling optimization extends the traditional combinatorial optimization problem, adding constraints and variables, and becomes a multi-constraint unit commitment (MCUC)problem. In this paper, a novel PEV scheduling optimization framework is proposed to address MCUC problem, taking economic cost and carbon emissions as the objectives, a multi-constraint mathematical model is established. Meanwhile, a new binary level-based learning optimization algorithm is proposed to solve flexible scheduling of PEVs in power systems. Finally, considering different numbers of units, the superiority of the proposed optimization algorithm is verified by comparison with different algorithms. The feasibility and effectiveness of the proposed solution are also analyzed before and after scheduling to verify its potential in solving the MCUC problem.