Classical optimal power flow problem is an important optimization problem of power system. Renewable energy can produce electricity with near zero pollution, with the increasing popularity of renewable energy, it becomes more and more urgent to study the optimal power flow problem with renewable energy. In this paper, the standard IEEE30 bus is modified to incorporate renewable energy, and the multi-objective optimal power flow (MOOPF) problem is studied. The MOOPF problem optimization objectives include generation cost, emission, real power loss and voltage deviation (VD). Three renewable energy sources with successful industrial applications including wind energy, solar energy and tidal energy been introduced. Weibull distribution probability, lognormal probability and Gumbel probability are used to calculate the instability and intermittency of wind energy, solar energy and tidal energy, respectively. In order to solve the multi-objective optimal power flow problem with multiple renewable energy sources, a named multi-objective pathfinder algorithm (MOPFA) based on elite dominated set and crowding distance is proposed. Simulation results show that MOPFA can get more evenly distributed Pareto front and provide more diverse solutions. A Compromise solution was selected by the fuzzy decision system. The compromise solution obtained by MOPFA can effectively dispatch the power distribution of the system, and is more inclined to use renewable energy, reducing the power used by thermal power units, which can significantly minimize emissions and other optimization objective. Comparison with the recently published literature also shows that the proposed model can effectively reduce emission and other indicator. In addition, the statistical test results show that MOPFA's multi-objective optimization performance ranks first.