This　paper　proposes　a　novel　grid-friendly　multi-objective　approach　to　optimize　energy　management　in　an　integrated　source-grid-load-storage　microgrid　(MG).　To　enhance　the　MG＇s　grid　integration　potential　and　cost-effectiveness,　this　approach　develops　a　grid-friendly　multi-timescale　energy　scheduling　optimization　(Gf-MtESO)　strategy　and　a　new　evaluation　metric　().　Gf-MtESO　first　establishes　electricity　market　coordination　by　pre-submitting　energy　demand　as　subsequent　scheduling　constraints,　effectively　mitigating　power　exchange　fluctuations　between　MGs　and　the　main　grid.　Additionally,　,　by　holistically　evaluating　dependency　and　volatility,　facilitates　comprehensive　assessment　of　MGs＇　grid　integration　potential.　To　resolve　conflicting　objectives　and　multi-constraints　challenges　in　developing　the　Gf-MtESO　strategy,　this　approach　applies　an　improved　elitist　non-dominated　sorting　genetic　algorithm　based　on　stepwise-solving　and　rotating-population　optimization　(SRO-NSGA-II).　SRO-NSGA-II　first　decouples　the　problem　and　updates　the　population　using　rotated　binary　crossovers　to　accelerate　the　search　for　feasible　domains.　Results　indicate　that　SRO-NSGA-II　concurrently　maintains　solution　diversity　and　convergence　speed,　outperforming　NSGA-II　in　hypervolume　metrics.　Particularly,　the　novel　approach　demonstrates　faster　scheduling　plans　development　and　improves　grid-connection　friendliness　by　90.76%　with　a　4.86%　cost　variation　compared　to　benchmark　methods,　which　provide　a　systematic　approach　to　realize　friendly　grid　integration　while　ensuring　economic　viability　in　MGs＇　applications.