Multi-objective optimal power flow (OPF) focusing on fuel cost generation and emission with wind power integration is solved by proposed a SWTCM technique developing with non-dominated sorting (NS) and PSO algorithms. The SWTCM_NSPSO develops the balancing search solution competency of global best investigation and use of local best including the stochastic weight trade-off mechanism cooperating with coefficients with dynamistic trade-off technique. This improved algorithm combines with chaotic mutation to increase search efficiency, diversion, and prevent premature convergence problem. Crowding distance (CD) and NS approaches remarkably optimize the best Pareto front cooperating with Fuzzy selecting function for providing the local best solution. Two stages method is created to choose the best optimum resolution (global) from many trials of best compromise group (local). The modified IEEE 30 bus test system is investigated by using SWTCM_NSPSO. The simulation results clearly simulated a lower values set and better Pareto fronts distribution curve than other methods e.g. simple NSGAII and NSPSO, chaotic deviation combined with non-dominated sorting PSO, and trade-off stochastic method integrating with NSPSO leading to save operation fuel cost and reduce pollutant emission through provide a better multi- objective trade-off solution.